05-model-evaluation-and-improvement.ipynb

{
 "cells": [
  {
   "cell_type": "code",
   "execution_count": 76,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/var/folders/l_/499_8k_5387b9tz3wp5g8lg00000gn/T/ipykernel_95387/2603895367.py:13: DeprecationWarning: `set_matplotlib_formats` is deprecated since IPython 7.23, directly use `matplotlib_inline.backend_inline.set_matplotlib_formats()`\n",
      "  set_matplotlib_formats('pdf', 'png')\n"
     ]
    }
   ],
   "source": [
    "#from preamble import *\n",
    "#%matplotlib inline\n",
    "#=================================\n",
    "from IPython.display import set_matplotlib_formats, display\n",
    "import pandas as pd\n",
    "%matplotlib inline\n",
    "\n",
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import mglearn\n",
    "from cycler import cycler\n",
    "\n",
    "set_matplotlib_formats('pdf', 'png')\n",
    "plt.rcParams['savefig.dpi'] = 300\n",
    "plt.rcParams['image.cmap'] = \"viridis\"\n",
    "plt.rcParams['image.interpolation'] = \"none\"\n",
    "plt.rcParams['savefig.bbox'] = \"tight\"\n",
    "plt.rcParams['lines.linewidth'] = 2\n",
    "plt.rcParams['legend.numpoints'] = 1\n",
    "plt.rc('axes', prop_cycle=( cycler('color', mglearn.plot_helpers.cm_cycle.colors) +  cycler('linestyle', ['-', '-', \"--\", (0, (3, 3)), (0, (1.5, 1.5))])))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model Evaluation and Improvement\n",
    "\n",
    "- We have seen many ML algorithms and how to prepare our data to select best feature. We will now dive more deeply into evaluating models and selecting parameter of unsupervised approach <br></br>\n",
    "- Model evaluation, Model performance , Performance Measures <br></br>\n",
    "\n",
    "![](./images/mode_performance.jpeg)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Three different APIs for evaluating the quality of a model’s predictions:\n",
    "\n",
    "- Estimator score method: **Estimators have a score method** providing a default evaluation criterion for the problem they are designed to solve. <br><br>\n",
    "\n",
    "- Scoring parameter: Model-evaluation tools using cross-validation (such as model_selection.cross_val_score and model_selection.GridSearchCV) rely on an internal scoring strategy. <br><br>\n",
    "\n",
    "- Metric functions: The sklearn.metrics module implements functions assessing prediction error for specific purposes. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- We have been using score to estimate the performance "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 77,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.880\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import make_blobs\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# create a synthetic dataset\n",
    "X, y = make_blobs(random_state=0)\n",
    "# split data and labels into a training and a test set\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "# instantiate a model and fit it to the training set\n",
    "logreg = LogisticRegression().fit(X_train, y_train)\n",
    "# evaluate the model on the test set\n",
    "score = logreg.score(X_test, y_test)\n",
    "print(f\"Test set score: {score:.3f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- This chapter will introduce better ways of evaluating model that go beyond simple accuracy<br><br>\n",
    "  "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Simply splitting the data into training and test set doesn't allow to have a real idea of the model performance. You can say “Wow”, 90% of accuracy on the training set, but after you see 60% of test accuracy. Some of the issues related to this are:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \n",
    "> The machine learning methods often fail to model the data because they learn particular features of the training set, that are not present in the test set. So these features are not representative and we are in a situation of Overfitting. It happens when the model fits too much of the training data, but it’s not able to generalize in new samples. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> But, Holdout Method suffers from issues of high variance. This is because it is not certain which data points will end up in the validation set and the result might be entirely different for different sets ( the results can depend on a particular random choice for the pair of (train, validation) sets.)\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/balanced-or-imbalanced.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Also, partitioning the available data into two sets, we drastically reduce the number of samples which can be used for learning the model.More data will usually result in more accurate models."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> There are many ways to address this issue: regularization, selection of the best hyperparameters, and K fold cross validation."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Cross-Validation\n",
    "\n",
    "- A good way to evaluate a model is to use cross-validation <br><br>\n",
    "\n",
    "- Cross-validation is a statistical method of evaluating generalization performance that is more stable and thorough than using a split into a training and a test set.<br><br>\n",
    "- In cross-validation, the data is instead split repeatedly and multiple models are trained. <br><br>\n",
    "- The most commonly used version of cross-validation is k-fold cross-validation, where k is a user-specified number, usually 5 or 10.\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 78,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 864x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Cross-Validation in scikit-learn"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Cross-validation is implemented in scikit-learn using the **cross_val_score** function from the model_selection module. <br><br>\n",
    "\n",
    "- The parameters of the cross_val_score function are the model we want to evaluate, the training data, and the ground-truth labels. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " <center>\n",
    " cross_val_score: (estimator, X, y=None..)\n",
    "</center>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 79,
   "metadata": {},
   "outputs": [
    {
     "ename": "SyntaxError",
     "evalue": "invalid syntax (3820258140.py, line 8)",
     "output_type": "error",
     "traceback": [
      "\u001b[0;36m  File \u001b[0;32m\"/var/folders/l_/499_8k_5387b9tz3wp5g8lg00000gn/T/ipykernel_95387/3820258140.py\"\u001b[0;36m, line \u001b[0;32m8\u001b[0m\n\u001b[0;31m    scores = cross_val_score(logreg, .datirisa, iris.target) # SKlearn 3 fold\u001b[0m\n\u001b[0m                                     ^\u001b[0m\n\u001b[0;31mSyntaxError\u001b[0m\u001b[0;31m:\u001b[0m invalid syntax\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_val_score\n",
    "from sklearn.datasets import load_iris\n",
    "from sklearn.linear_model import LogisticRegression\n",
    "\n",
    "iris = load_iris()\n",
    "logreg = LogisticRegression(max_iter=1000)\n",
    "\n",
    "scores = cross_val_score(logreg, .datirisa, iris.target) # SKlearn 3 fold\n",
    "print(\"Cross-validation scores: {}\".format(scores))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> No more X_train, y_train...."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>5.1</td>\n",
       "      <td>3.5</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>4.9</td>\n",
       "      <td>3.0</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>4.7</td>\n",
       "      <td>3.2</td>\n",
       "      <td>1.3</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4.6</td>\n",
       "      <td>3.1</td>\n",
       "      <td>1.5</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5.0</td>\n",
       "      <td>3.6</td>\n",
       "      <td>1.4</td>\n",
       "      <td>0.2</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "     0    1    2    3\n",
       "0  5.1  3.5  1.4  0.2\n",
       "1  4.9  3.0  1.4  0.2\n",
       "2  4.7  3.2  1.3  0.2\n",
       "3  4.6  3.1  1.5  0.2\n",
       "4  5.0  3.6  1.4  0.2"
      ]
     },
     "execution_count": 10,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pd.DataFrame(iris.data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
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     "execution_count": 88,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(iris.target).head()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "By default, cross_val_score performs three-fold cross-validation, returning three accuracy values. We can change the number of folds used by changing the cv parameter\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores: [0.96666667 1.         0.93333333 0.96666667 1.        ]\n"
     ]
    }
   ],
   "source": [
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=5)\n",
    "print(\"Cross-validation scores: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "A common way to summarize the cross-validation accuracy is to compute the mean:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cross-validation score: 0.9733333333333334\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average cross-validation score: {scores.mean()}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average cross-validation score: 0.973\n"
     ]
    }
   ],
   "source": [
    "print(f\"Average cross-validation score: {scores.mean():.3f}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Why we use mean? The mean is used to summarize a data set. It is a measure of the center of a data set. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Using the mean cross-validation we can conclude that we expect the model to be around 96% accurate on average. Looking at all five scores produced by the five-fold cross-validation, we can also conclude that there is a relatively high variance in the accuracy between folds, ranging from 100% accuracy to 90% accuracy. This could imply that the model is very dependent on the particular folds used for training, but it could also just be a consequence of the small size of the dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/kfold_summary.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Dis-advantage of cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The main disadvantage of cross-validation is increased computational cost. <br><br>\n",
    "- As we are now training k models instead of a single model, cross-validation will be roughly k times slower than doing a single split of the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is important to keep in mind that cross-validation is not a way to build a model that can be applied to new data. Cross-validation does not return a model. When calling cross_val_score, multiple models are built internally, but the purpose of cross-validation is only to evaluate how well a given algorithm will generalize when trained on a specific dataset.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<centre>\n",
    "\n",
    "![](./images/inner_working_train_test.png)\n",
    "\n",
    "</centre>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can also specify to return the performance on each train and test set , by specifying return_train_score = TRUE"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "{'fit_time': array([0.07288694, 0.14203906, 0.09248686, 0.07231116, 0.06285214]),\n",
       " 'score_time': array([0.000494  , 0.00342488, 0.00075912, 0.00045896, 0.00050473]),\n",
       " 'test_score': array([0.96666667, 1.        , 0.93333333, 0.96666667, 1.        ]),\n",
       " 'train_score': array([0.96666667, 0.96666667, 0.98333333, 0.98333333, 0.975     ])}"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "res = cross_validate(logreg, iris.data, iris.target, cv=5,\n",
    "                     return_train_score=True)\n",
    "display(res)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_score</th>\n",
       "      <th>train_score</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.072887</td>\n",
       "      <td>0.000494</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.142039</td>\n",
       "      <td>0.003425</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.966667</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.092487</td>\n",
       "      <td>0.000759</td>\n",
       "      <td>0.933333</td>\n",
       "      <td>0.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.072311</td>\n",
       "      <td>0.000459</td>\n",
       "      <td>0.966667</td>\n",
       "      <td>0.983333</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.062852</td>\n",
       "      <td>0.000505</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.975000</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_score  train_score\n",
       "0  0.072887    0.000494    0.966667     0.966667\n",
       "1  0.142039    0.003425    1.000000     0.966667\n",
       "2  0.092487    0.000759    0.933333     0.983333\n",
       "3  0.072311    0.000459    0.966667     0.983333\n",
       "4  0.062852    0.000505    1.000000     0.975000"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "res_df = pd.DataFrame(res)\n",
    "display(res_df)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Mean times and scores:\n",
      " fit_time       0.367992\n",
      "score_time     0.000904\n",
      "test_score     0.973333\n",
      "train_score    0.975000\n",
      "dtype: float64\n"
     ]
    }
   ],
   "source": [
    "print(\"Mean times and scores:\\n\", res_df.mean())"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Stratified k-Fold Cross-Validation and Other Strategies\n",
    "\n",
    "- Splitting the dataset into just k folds might not always be a good idea. <br><br>\n",
    "\n",
    "- Stratified K Fold used when just random shuffling and splitting the data is not sufficient, and we want to have correct distribution of data in each fold. <br><br>\n",
    "\n",
    "- In case of regression problem folds are selected so that the mean response value is approximately equal in all the folds. <br><br>\n",
    " \n",
    "- In case of classification problem folds are selected to have same proportion of class labels. Stratified K Fold is more useful in case of classification problems, where it is very important to have same percentage of labels in every fold. <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/stratified_kfold.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let us demonatrate how K-fold cross-validation may fail"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris labels:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(\"Iris labels:\\n{}\".format(iris.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As the simple k-fold strategy fails here, scikit-learn does not use it for classification, but rather uses strati#ed k-fold cross-validation. In stratified cross-validation, we split the data such that the proportions between classes are the same in each fold as they are in the whole dataset\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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+Clwr6cSCyNUF//eud3kknSpplqRZQFIfWp13PvV8NVI7CDvvvPPOOz888maDqdm6AhIRK4GZwExJ9wNTgKm9owujQ1CWS4BLIPtRxpQ+tDrvfOr5SqV2EHbe+ZTzQFLlcd751PNmg6mpzlhJ2lnSTgWDxgNPFDw/puD/H/uZ3CvABoNXOpL60Oq886nnK5HiQdh551PN90qlPM473wx5s8HUVA0rYH1gmqQHJM0HdgE6CsavJ+ke4LPAWf1M6xLgJkkzikdIOkPSU8C2wHxJPx6Mwqf8Idd551PLp3oQdt75FPOFUiiP8843S95sMCmi7j3mhkR+V8A9Cu8UOMTzj46OjpLjU/vQ6rzzjc53dHQQESrMSYqISPog7LzzKeUlvaUe9dahZim/8843Ol9ch8xq0WxnrJpSM3zIdd75VPKpH4Sddz6lfLVSK7/zzqecN6tW0928opSIGN3oMvQltQ+tzjufch5I6qDqvPOp56uRYvmddz7VvNlA+IxVHaX2odV551PO90rloOq8882Qr1Sq5Xfe+RTzZgM1bK6xarTW1talPT09oxpdDrNm0dLSsqy7u3vLwmHt7e3Lurq6tmhUmcyaTVtb27LOzs7V9ai9vX1pV1eXj0VmFSquQ2a1cMPKzMzMzMysRu4KaGZmZmZmVqN+G1aSPitpQ2Uuk3SfpIOGonBmZmZmZmbNoJIzVidHxMvAQcDbgZOA79S1VGZmZmZmZk2kkoaV8v+HAD+NiHkFw8zMzMzMzNZ6lTSsZku6maxh9VtJGwCr6lssMzMzMzOz5tHvXQElrQOMBx6PiBclbQZsExHzh6B8ZmZmZmZmyWvpLxARqyQtA3aR1G/ezMzMzMxsbdNvQ0nSd4FjgAeAlfngAG6vY7nMzMzMzMyaRiVdAf8MjIuIFUNTJDMzMzMzs+ZSSde+x4FWwA2rMtrb25d2dXWNanQ5zJpFW1vbss7Ozi0Lh7W0tDy3cuXKTRtVJrNm09LS8mx3d/fqY4+PRWbV6etYZDZQlZyx+jnwHuB3FDSuIuKM+hatuUiKwmU5c+ZMJk+ezPTp05k4cWK/r3fe+bUtL4mIUNGw6Ojo6Hf6ixYtYvr06UyePJkxY8Y47/xam582bdpb6lHxsQjSqO/OO59qvq9jkdlAVXLG6ob8zyrU6J2E8843U75aKX/Idd751PKp1XfnnU85b1arSu4KOE3SusC78kF/joju+hareaW2k3De+ZTz1UrtQ6vzzqecT62+O+98ynmzwVDJXQEnAtOAxYCA7SRNiQjfFbBIajsJ551POV+t1D60Ou98yvnU6rvzzqecNxss61SQ+S/goIjYLyL2BT4MnFvfYpUm6WxJCyXNlzRX0l795DskfS5//DVJB+aPz5Q0ssRrrpT0Z0kLJP1EUmslZUtpJ+G886nnq5Hah1bnnU85n2J9d975VPNmg6mShlVrRPy590lEPEx2l8AhJ2lv4DBgQkSMAw4Enqz09RHxlYi4NX96JtBnwwq4Eng3sBvQDnyykumnspNw3vlmyFcqtQ+tzjufch78JZ/zztfrSz6z/lTSsJol6TJJE/O/S4HZ9S5YCVsBy3t/UysilkfEEgBJiyV9V9Kf8r8di18saaqkoyWdAWwNzJA0ozgXEb+OHPAnYNtKCpfKTsJ555shX4nUPrQ673zK+V4p1nfnnU81bzaYKmlYfRpYCJwBfBZ4ADitnoUq42aya7welnSxpP2Kxr8cEe8DLgTOKzWRiDgfWAJMiohJpXJ5F8ATgN+UGH+qpFmSZlVS+FR3Ks47n2I+tQ+tzjufcr5QCvXXeeebJW82mCq5K+AK4Pv5X0NFxKuSdgf2ASYB10r6YkRMzSNXF/wfjOvALgZuj4g7SpTnEuASyH47pNyEUt6pOO98avnUPrQ673zK+WqlVt+ddz7lvFk1SjasJF0XER+VdD+wRqMhv8ZpyEXESmAmMDMv2xRgau/owmgt85H0VeDtwKdqmQ6kt5Nw3vmU86l9aHXe+ZTz1UqtvjvvfMp5s2qVO2P12fz/YUNRkEpI2hlYFRGP5IPGA08URI4BvpP//2M/k3sF2ABY3sd8Pkl298MDImJVLWVObSfhvPMp54GkPrQ673zq+WqkVt+ddz7lvNlAlGxYRcQz+cN/jIgvFI6T9F3gC2u+qu7WBy6QtDHQAzwKnFowfj1J95BdO3ZcP9O6BLhJ0jN9XGf1I7IG2x8lAfxPRHyt2sKmtpNw3vmU871S+tDqvPOp5yuVWn133vmU82YDpezGd2UC0n0RMaFo2PxGdQUsRdJiYI+IWOMM1BDNPwqXZWo7CeedTy0viYhQ0bDo6Ojod/qpfsh13vmhzk+bNu0t9aj4WARp1HfnnU8139exyGygyl1j9WngH4HtJc0vGLUBcGe9C9bMGr2TcN75ZspXK+UPuc47n1o+tfruvPMp581qVe4aq6uAm4BvA18sGP5KRDxf11INQESMbnQZIL2dhPPOp5yvVmofWp13PuV8avXdeedTzpsNhn67Aq4OSlsAbb3PI+Iv9SpUM2pvb1/a1dU1qtHlMGsWbW1tz3Z2dr6lzrS2ti7t6elxPTKrUEtLy7Lu7u4te5/7WGRWnba2tmWdnZ1b9p80618l11h9hOw3rLYGngXeCTwYEbvWv3hmZmZmZmbpW6eCzDeA9wMPR8QY4AB8jZWZmZmZmdlqlTSsuiPiOWAdSetExAyy348yMzMzMzMzyt+8oteLktYHbgeulPQs2W9ImZmZmZmZGZVdY/U2oJPs7NbHgI2AK/OzWGZmZmZmZmu9sg0rSSOA30bEgUNXJDMzMzMzs+ZS9hqriFgJvC5poyEqj5mZmZmZWdOp5BqrLuB+SbcAr/UOjIgz6lYqMzMzMzOzJlJJw+pX+Z+ZmZmZmZn1od+bVwBIagfeERF/rn+RzMzMzMzMmku/Z6wkfQT4T2BdYIyk8cDXIuLwOpetqbS3ty/t6uoa1ehymDWLtra2ZZ2dnVsWDnM9MqtOcT1qbW1d2tPT4zpkVqGWlpZl3d3dW/afNOtfJbdbnw3sD8yMiPfmw+6PiN2GoHxNQ1LMmDGDyZMnM336dCZOnNjva2bOnOm882ttXhIRoaJhUWqflFr5nXc+hXxxPZIUHR0dLFq0iOnTpzN58mTGjBnT7/Sdd35tzXd0dKxxLDIbqLJ3Bcz1RMRLRcP67z+4FmqGg7DzzqeSr0aK5Xfe+VTzKX1odd751PNmg6mSm1cskHQ8MELSTsAZwF31LVZzSuWg6rzzzZCvVKrld975FPNAUh9anXc+9bzZYKrkjNVngF2BFcBVwEvAZ+tZqGaVwkHVeeebJV+JlMvvvPOp5Xul9KHVeedTz5sNpkoaVodGxNkRsWf+929Aw25cIelsSQslzZc0V9Je/eQ7JH0uf/w1SQfmj8+UNLLEay6TNC+fx/WS1h+Msqd2EHbeeeedd3545Aul9KHVeedTz5sNpkoaVl+qcFjdSdobOAyYEBHjgAOBJyt9fUR8JSJuzZ+eCfTZsALOioj35PP4C3D6wEudSe0g7Lzzzjvv/PDIVyvlD7nOO59a3qwaJa+xknQwcAiwjaTzC0ZtCPTUu2AlbAUsj4gVABGxvHeEpMXAtcCkfNDxEfFo4YslTQVuBLbO/2ZIWh4RkwpzEfFynhfQTo0360jtIOy888477/zwyFcrtQ+tzjufct6sWuXOWC0BZgFdwOyCvxuAD9e/aH26GdhO0sOSLpa0X9H4lyPifcCFwHmlJhIR55O9v0nFjapekn4KLAXeDVxQInOqpFmSZpWaV2oHYeedTzkPJFUe551PPV+N1D60Ou98ynmzgSh5xioi5gHzJF0ZEY06Q/UWEfGqpN2BfcjOTF0r6YsRMTWPXF3w/9wa53WSpBFkjapjgJ/2kbkEuASy3w4pHp/iQdh551PN90qlPM473wz5SqX2odV551POmw1UyTNWkq7LH87Jb+Lwlr8hKt8aImJlRMyMiK+SXft0VOHoEo8HPC+y7oVH9ZctlupB2HnnU8wXSqE8zjvfLPlKpPah1XnnU86b1aLc71j13lL9sKEoSCUk7QysiohH8kHjgScKIscA38n//7Gfyb0CbAAsLxyYX1e1Q0Q8mj/+CPBQNeVM+SDsvPOp5auVWvmddz7lfGofWp13PuW8Wa3KdQV8Jv//RKlMA6wPXCBpY7IbaDwKnFowfj1J95CdiTuun2ldAtwk6Zmi66wETJO0Yf54HvDpSguY2kHVeedTzlcrtfI773zK+dQ+tDrvfMp5s8FQ7oxVciJiNvCBMpGLIuKcotd0FDw+seDxBfRxU4qIWAX87UDKl9pB1XnnU85XK7XyO+98yvnUPrQ673zKebPBUsnvWFmFUjqoOu986vlqpFh+551PNZ/ah1bnnU85bzaYyt284nOSthvKwtQiIkYX/q5VI6RyUHXe+WbIVyrV8jvvfIp5IKkPrc47n3rebDApou+b50k6FzgaWER2+/LpjW64pKy9vX1ZV1fXFo0uh1mzaGtrW9bZ2bll4bD29valXV1doxpVJrNmU1yPWlpanlu5cuWmjSyTWTNpaWl5tru728cdGxQlG1aw+g55+wLHAn9PdiOHq4FfRMQrQ1JCMzMzMzOzxJVtWL0lmP1Y7oFktzPfOSJG1rNgZmZmZmZmzaKiuwJK2o3srNUxwHPAl+tZKDMzMzMzs2ZSsmElaSey34I6FlgJXAMcFBGPD1HZzMzMzMzMmkK5m1c8TnY91TURcf+QlsrMzMzMzKyJlGtY7QSMiog/FA3fB1gSEY8NQfnMzMzMzMySV+4Hgs8FXu5jeCdwXl1KY2ZmZmZm1oTKNaxGR8T84oERMQsYXbcSmZmZmZmZNZlyDau2MuPaB7sgZmZmZmZmzapcw+peSacUD5T0CWB2/YpkZmZmZmbWXMrdvGIU8AvgDd5sSO0BrAscGRFLh6SETaK9vX1pV1fXqEaXw6xZtLW1Levs7NyycJjrkVl1iuuR65BZdfo6FpkNVMmG1eqANAkYmz9dGBG/r3upmpCk6G9ZmtmbJBERKhrmemRWheJ65DpkVp2+jkVmA9Vvw8oq44OZWXXcsDKrnRtWZrVxw8oGU7lrrMzMzMzMzKwCbliZmZmZmZnVqG4NK0lbSrpG0mOSHpD0a0nvkjRa0oI6zbND0uf6yUyVdHQV06xbec3MzMzMbHhoqcdEJYnsjoLTIuLYfNh4YBTwZD3maWZmZmZm1ij1OmM1CeiOiB/1DoiIuRFxR2EoPxt0h6T78r8P5MO3knS7pLmSFkjaR9KI/GzTAkn3SzqrXAEknSLpXknzJP1c0siC0Qfm831Y0mF5foSk7+WvmS/pU4O3OKxWS5cu5dhjj2WHHXZgl1124ZBDDuHhhx9m8eLFjB07tv8JDMCKFSs45phj2HHHHdlrr71YvHhxXeZjNhQaUYduv/12JkyYQEtLC9dff31d5mE2lBpRj77//e+zyy67MG7cOA444ACeeOKJuszHzGpXr4bVWCr7EeFngQ9FxATgGOD8fPjxwG8jYjzwHmAuMB7YJiLGRsRuwE/7mfb/RMSeEfEe4EHgEwXjRgP7AYcCP5LUlo9/KSL2BPYETpE0poL3YHUWERx55JFMnDiRxx57jAceeIBvfetbLFu2rK7zveyyy9hkk0149NFHOeuss/jCF75Q1/mZ1Uuj6tA73vEOpk6dyvHHH1/X+ZgNhUbVo/e+973MmjWL+fPnc/TRR/P5z3++rvMzs4Fr9M0rWoFLJd0PTAd2yYffC5wkqQPYLSJeAR4Htpd0gaS/A17uZ9pj87NS9wMfA3YtGHddRKyKiEfy6b4bOAj4B0lzgXuAzYCdys1A0qmSZkmaVflbtmrNmDGD1tZWTjvttNXDxo8fzz777POW3OLFi9lnn32YMGECEyZM4K677gLgmWeeYd9992X8+PGMHTuWO+64g5UrV3LiiScyduxYdtttN84999w15vvLX/6SKVOmAHD00Ufzu9/9Dt/G2JpRo+rQ6NGjGTduHOus0+hDjVntGlWPJk2axMiRWaeb97///Tz11FN1fJdmVou6XGMFLAQquUHEWcAysrNS6wBdABFxu6R9yc4o/UzS9yLicknvAT4M/BPwUeDkMtOeChwREfMknQhMLBhX/Ok4AAGfiYjfFo6QNLrUDCLiEuCSPOdP3HWyYMECdt99935zW2yxBbfccgttbW088sgjHHfcccyaNYurrrqKD3/4w5x99tmsXLmS119/nblz5/L000+zYEF2X5IXX3xxjek9/fTTbLfddgC0tLSw0UYb8dxzz7H55psP6vszq7dG1SGz4SSFenTZZZdx8MEHD8bbMbM6qFfD6vfAtySdEhGXAkjaExgJFHYO3gh4KiJWSZoCjMiz7wSejohLJb0NmCDp18AbEfFzSY+RNZzK2QB4RlIr2RmrpwvGTZY0DRgDbA/8Gfgt8GlJv4+IbknvKnqNJa67u5vTTz+duXPnMmLECB5++GEA9txzT04++WS6u7s54ogjGD9+PNtvvz2PP/44n/nMZzj00EM56KCD1pheX2ensvuymA1Pg12HzNZG9apHV1xxBbNmzeK2224bqrdiZlWqS/+M/GffjwQ+lN9ufSHQASwpil4MTJF0N/Au4LV8+ERgrqQ5wFHAD4BtgJl5V72pwJf6Kca/k3XpuwV4qGjcn4HbgJuA0yKiC/gx8ABwX3579f+mfg1Pq8Kuu+7K7Nn9X7J37rnnMmrUKObNm8esWbN44403ANh33325/fbb2WabbTjhhBO4/PLL2WSTTZg3bx4TJ07koosu4pOf/OQa09t222158snsJpY9PT289NJLbLrppoP75syGQKPqkNlw0sh6dOutt/LNb36TG264gfXWW29Q35eZDZ66NRwiYglZd72+jM0zjwDjCoZ/KR8+DZjWx+sm9DPPjoLHPwR+2EfmxBKvXQV8Of8r9FJvea0x9t9/f7785S9z6aWXcsoppwBw77338vrrr/POd75zde6ll15i2223ZZ111mHatGmsXLkSgCeeeIJtttmGU045hddee4377ruPQw45hHXXXZejjjqKHXbYgRNPPHGN+R5++OFMmzaNvffem+uvv57999/fZ6ysKTWqDpkNJ42qR3PmzOFTn/oUv/nNb9hiiy2G5L2a2cDIF+MPDknhZVk/S5Ys4cwzz2T27Nm0tbUxevRozjvvPFpbWznssMNYsGABjzzyCEcddRQjR45k0qRJXHDBBbz66qtMmzaN733ve7S2trL++utz+eWX8/LLL3PSSSexatUqAL797W+v0W+9q6uLE044gTlz5rDppptyzTXXsP322zfi7Q9LkogIFQ1zPaqTRtShe++9lyOPPJIXXniBtrY2ttxySxYuXNiItz9sFdcj16H6akQ9OvDAA7n//vvZaqutgOxumzfccMOQv/fhqq9jkdlAuWE1SHwwM6uOG1ZmtXPDyqw2bljZYPI9cM3MzMzMzGrkmzMMkra2tmWSRjW6HGbNoq2tbY1f1XQ9MqtOcT1yHTKrTl/HIrOBcldAMzMzMzOzGrkroJmZmZmZWY3csDIzMzMzM6uRG1ZmZmZmZmY1csPK1hqSTm10GcyameuQWe1cj8yGLzesbG3ig5lZbVyHzGrnemQ2TLlhZWZmZmZmViM3rMzMzMzMzGrkhpWtTS5pdAHMmpzrkFntXI/Mhin/QLCZmZmZmVmNfMbKzMzMzMysRm5YWdORtFLS3IK/0WWyUyUd3cfwiZJu7GP4ZpJmSHpV0oWDXHSzJNS5Dn1I0mxJ9+f/9x/k4psloc716H0F050n6chBLr6Z1UFLowtgNgCdETG+TtPuAv4dGJv/mQ1H9axDy4GPRMQSSWOB3wLb1GleZo1Uz3q0ANgjInokbQXMk/R/EdFTp/mZ2SDwGSsbFiSNl3S3pPmSfiFpkz4yfyfpIUl/AP5fX9OJiNci4g9kDSyztcYg1qE5EbEkf7oQaJO0Xh2LbpaMQaxHrxc0otoAXxBv1gTcsLJm1F7QReIX+bDLgS9ExDjgfuCrhS+Q1AZcCnwE2AfYcigLbJaYoapDRwFzImLF4BXdLBl1rUeS9pK0MJ/OaT5bZZY+dwW0ZvSW7heSNgI2jojb8kHTgOlFr3k3sCgiHslfcwVw6hCU1SxFda9DknYFvgscNIjlNktJXetRRNwD7Crpb4Bpkm6KCPemMEuYz1jZ2sRdKcxqU1EdkrQt8AvgHyLisfoWyazpVHUsiogHgdfwdb9myXPDyppeRLwEvCBpn3zQCcBtRbGHgDGSdsifHzdU5TNL3WDWIUkbA78CvhQRd9ahuGZJGuR6NEZSS/74ncDOwOJBL7SZDSp3BbThYgrwI0kjgceBkwpHRkSXpFOBX0laDvyBEt/+SVoMbAisK+kI4KCIeKCOZTdLwWDVodOBHYF/l/Tv+bCDIuLZ+hXdLBmDVY8+CHxRUjewCvjHiFhe36KbWa0U4d5RZmZmZmZmtXBXQDMzMzMzsxq5YWVmZmZmZlYjN6zMzMzMzMxq5IaVmZmZmZlZjdywMjMzMzMzq5EbVmZmw4CklZLmSlooaZ6kf5ZUdh8vabSk44egbD+WtEs/mSP6y5iZmaXMDSszs+GhMyLGR8SuwIeAQ4Cv9vOa0UDdG1YR8ckKfgvuCMANKzMza1puWJmZDTP5j/GeCpyuzGhJd0i6L//7QB79DrBPfqbrrDK51fLMQ5KmSZov6fr8x1CRdICkOZLul/QTSevlw2dK2iN//Kqkb+Zn1e6WNCqfz+HA9/Ky7CDpDEkP5PO4ZiiWm5mZWS38A8FmZsOApFcjYv2iYS8A7wZeAVZFRJeknYCrI2IPSROBz0XEYXl+ZF+5ommOBhYBH4yIOyX9BHgAuBB4BDggIh6WdDlwX0ScJ2lmPp9ZkgI4PCL+T9J/AC9HxDckTQVujIjr8/ksAcZExApJG0fEi4O+0MzMzAaRz1iZmQ1fyv+3ApdKuh+YTukud5XmnoyIO/PHVwAfBHYGFkXEw/nwacC+fbz2DeDG/PFssu6IfZkPXCnp40BPiYyZmVky3LAyMxuGJG0PrASeBc4ClgHvAfYA1i3xskpzxV0dgjcbcf3pjje7SqwEWkrkDgUuAnYHZksqlTMzM0uCG1ZmZsOMpLcDPwIuzBsxGwHPRMQq4ARgRB59Bdig4KWlcsXeIWnv/PFxwB+Ah4DRknbMh58A3FZFsVeXJb+b4XYRMQP4PLAxsH7pl5qZmTWeG1ZmZsNDe+/t1oFbgZuBc/JxFwNTJN0NvAt4LR8+H+jJbyRxVplcsQfz3HxgU+CHEdEFnARMz7sSriJr3FXqGuBfJc0BdgKuyKczBzjX11iZmVnqfPMKMzOrWH7zihsjYmyjy2JmZpYSn7EyMzMzMzOrkc9YmZmZmZmZ1chnrMzMzMzMzGrkhpWZmZmZmVmN3LAyMzMzMzOrkRtWZmZmZmZmNXLDyszMzMzMrEZuWJmZmZmZmdXIDSszMzMzM7MauWFlZmZmZmZWIzeszMzMzMzMauSGlZmZmZmZWY3csDIzMzMzM6uRG1ZmZmZmZmY1csPKzMzMzMysRm5YmZmZmZmZ1cgNKzMzMzMzW4OkzSTNzf+WSnq64Pm6/bx2D0nnVzCPuwavxG+Z7kxJe/STOVPSyEGbZ0QM1rTMzMzMzKyO2tvbl3Z1dY0arOm1tbUt6+zs3LK/nKQO4NWI+M+CYS0R0TNYZRlMkmYCn4uIWWUyi4E9ImL5YMzTZ6zMzMzMzJpEV1fXqIig92/GjBlsvvnmzJgxg8Lhpf6K89U20iRNlfR9STOA70p6n6S7JM3J/++c5yZKujF/3CHpJ/lZpMclnVEwvVcL8jMlXS/pIUlXSlI+7pB82B8knd873aJytUu6RtJ8SdcC7QXjfihplqSFks7Jh50BbA3MyN9Ln7lqtFT7AjMzMzMza7yZM2cyefJkpk+fzsSJEwc9X8a7gAMjYqWkDYF9I6JH0oHAt4Cj+njNu4FJwAbAnyX9MCK6izLvBXYFlgB3An8raRbw3/k8Fkm6ukSZPg28HhHjJI0D7isYd3ZEPC9pBPA7SeMi4nxJ/wxMKjhj1VdufqULxWeszMzMzMyaTAMbVQDTI2Jl/ngjYLqkBcC5ZA2jvvwqIlbkjZhngb7OlP0pIp6KiFXAXGA0WYPs8YhYlGdKNaz2Ba4AyBtDhQ2ij0q6D5iTl2+XEtOoNNcnN6zMzMzMzJpIgxtVAK8VPP46MCMixgIfAdpKvGZFweOV9N1zrq+MqijXGjePkDQG+BxwQESMA37VVxkrzZXjhpWZmZmZWRNpcKOq2EbA0/njE+sw/YeA7SWNzp8fUyJ3O/AxAEljgXH58A3JGoIvSRoFHFzwmlfIuib2l6uIr7EyMzMzM2siCTWqAP4DmJZfr/T7wZ54RHRK+kfgN5KWA38qEf0h8FNJ88m6Ef4pf/08SXOAhcDjZNdu9boEuEnSMxExqUyuIr7dupmZmZlZk2hvb1/W1dW1xWBNr9LbrTeSpPUj4tX8LoEXAY9ExLmNLlcxN6zMzMzMzCxZks4CpgDrkt1Y4pSIeL2xpVqTG1ZmZmZmZmY18s0rzMzMzMzMauSGlZmZmZmZWY3csDIzG2KSPibp5oLnfyvpEUmvSjpC0k2Spgxw2jMlfXLwSpuuwvdavEzLZQcwn3fk62bEQMtqZmbDnxtWZrZWk/RBSXdJeknS85LulLRnPu5ESX+ocfqjJYWk1T9vERFXRsRBBbGvARdGxPoR8b8RcXBETKtlvmXK8y5J0yUtz9/zfEn/3OyNhj6W6YBJWizpwIJp/yVfNysHY/pmZjY8uWFlZmstSRsCNwIXAJsC2wDn8NZffu9vGoPRIHkn2e9m1JWkHYB7gCeB3SJiI2AysAdv/kBiYd6/dWhmthaTtJmkufnfUklPFzxft4LXT5T0gQrntVjS5v1kvlxp2RvBDSszW5u9CyAiro6IlRHRGRE3R8R8SX8D/AjYO+8G9iKApKmSfijp15JeAyZJOlTSHEkvS3pSUkfBPG7P/7+YT2fvwjNhkh4Dtgf+Lx+/XnG3NUknS3pQ0guSfivpnQXjPiTpofzs04WAyrzfc4C7IuKfI+KZ/L3/OSKOj4gXC86ufULSX4DfS1pH0r9JekLSs5Iul7RRPu82SVdIek7Si5LuzX+tvvds3+OSXpG0SNLHiguTv9cXJY0tGPZ2SZ2StpC0iaQbJf01f+83Stq2rzdWfHax3HKRtIOk3+flXi7pSkkb5+N+BryjYH18vviso6StJd2Qn+F8VNIpBdPukHRdvpxekbRQ0h5l1omZWVVaW1uX5vukQflrbW1dWmpeEfFcRIyPiPFkx8Rze59HxBsVFHciUFHDqkJJN6z8baSZrc0eBlZKmgZcA9wdES8ARMSDkk4DPhkRHyx63fHAIcBhZL+p8X7gH8jOOo0FbpE0NyL+F9gXWARsHBE9AJJ27p1QROwgaXE+n1vz8atnJOkIsgPJR4BHgC8CVwMfyL/Z+zlwMvBL4HTgNOBnJd7vgcCXKlgu+wF/A6wCTsz/JgHPApcDFwInkP2myEbAdmRn+cYDnZLeBpwP7BkRf5a0FdkZwbeIiBWS/gc4Djg7H/xR4LaIeFbSZsBP82EjgJ/k8z6iXOErWC4Cvk3W6N0wz3YAZ0bECZL24a3rY3TRLK4mW9dbA+8mW9+PR8Tv8vGHA/8POAn4Rl7m95crs5lZpXp6ekZNmTKF6dOnM3nyZMaMGdPvaxYtWlQy39HRMaqa+UvaHfg+sD6wHDgxIp6RdAbZvrYHeIDseHUa2XH248BnIuKOgulsRrY/fTvwJ976Bdj/kh1b2oAfRMQlkr4DtEuaCyyMiI/1lavmvQw2n7Eys7VWRLwMfBAI4FLgr/mZiP4OMr+MiDsjYlVEdEXEzIi4P38+n+xAsd8gFfNTwLcj4sG8YfYtYHx+1uoQ4IGIuD4iuoHzgJLfPAKbAc9UMM+OiHgtIjqBjwHfj4jHI+JVsobZsfnZm+58mjvmZ/xm58sUskbZWEntEfFMRJTq6ngVWcOq1/H5sN5vSn8eEa9HxCvAN6lsuZZdLhHxaETcEhErIuKvZB8QKlpfkrYj22a+kK/7ucCPyRqavf4QEb/Or8n6GfCeSqZtZlapwWpUDYDIus8fHRG7k33h9c183BeB90bEOOC0iFjMW89y3VE0ra+S7S/fC9xA1lug18n59PcAzpC0WUR8EejMp/WxUrla32At3LAys7Va3mA5MSK2JTvbtDXZB/Fynix8ImkvSTPyLmsvkX1DV7afeBXeCfwg7zL3IvA82YFtm7ysq8sS2S++P9nXRHLPAVtVMM/CaWwNPFHw/Amy3g6jyBoNvwWukbRE0n9Iao2I14BjyJbDM5J+JendAHnXuFfzv32A35N9A7lX3lgcD/wiz46U9N/KuiG+THaGaWP1f11b2eWSdzO8Rtm1Ai8DV1D5+toaeD5v6BUuk20Knhc2bl8H2uTr1cxsEDWoUQWwHgU9M4B/A3q7aM8HrszPTvVUMK19yfa/RMSvgBcKxp0haR5wN9kZqZ1KTKPS3JBww8rMLBcRDwFTyQ4akJ3J6jNa9Pwqsm/btstvCPEj3uzSUGoalXoS+FREbFzw1x4Rd5GdfdquN6isD+F2pSYE3AocVcE8C8u8hKxx1+sdZAfMZRHRHRHnRMQuZH3oDyPrEklE/DYiPkTWkHuI7IwgEbFrfoe99SPijohYBVxHdtbqeODGgkbLvwA7A3tFxIZkB2Eofx0Z9L9cvp2/x3H5dD9eNM1y62wJsKmkwpt9vAN4up8ymZkNmgY1qiDbVy4suM5qt4I7sh4KXATsDsyu8AulNfa3kiaSdV3fOyLeA8wh6+o3oNxQcsPKzNZakt4t6V96b4iQd/M6juybL4BlwLbq/85HG5CdxeiS9D6yBkKvv5J1i9t+gMX8EfAlSbvmZdxI0uR83K+AXSX9v/wAdgawZZlpfZXs2qzvSdoyn96Oym5AsXGJ11wNnCVpjKT1yboiXhsRPZImSdotP4P0MlnXwJWSRkk6PL/WagXwKlDuVuVXkZ3h+lj+uNcGQCfZjT82zctfif6WywZ5mV6UtA3wr0WvX0aJ9RURTwJ3Ad9WdvOOccAngCsrLJuZWd3VqVEF2T797ZL2BpDUKmlXSeuQfbk4A/g8sDHZNViv0MddZ3O3k+33kXQwsEk+fCPghYh4Pe/tUHiNarek1gpyDeGGlZmtzV4B9gLuUXaHv7uBBWRnSiDrprYQWCppeZnp/CPwNUmvAF8hOwMDQES8Ttb//M68O19VO/6I+AXwXbLudi/n5Ts4H7ec7Hbp3yHr5rcTcGeZaT0G7A2MBhbm3RZ/DswiWxZ9+QlZl7/byW7C0QV8Jh+3JXA9WaPqQeA2sm4d65AtwyVkXRf3I1tGpcp1D/AaWTe7mwpGnQe0k10cfTfwm1LTKJpef8vlHGAC8BJZI+x/iibxbeDf8vX1uT5mcRzZMlxC1m3xqxFxSyVlMzOrtzo2qiD7ovBo4Lt5F7y5ZD0WRgBXSLqf7MzRuRHxIvB/wJHKbs++T9G0zgH2lXQfcBDwl3z4b4AWSfOBr/Pml50AlwDzJV3ZT64hlHU9NzMzMzOz1LW2ti7t6emp6k5+5YwYMeL5np6eht70Ybhww8rMzMzMzKxG7gpoZmZmZmZWIzeszMzMzMzMauSGlZmZmZmZWY3csDIzMzMzM6uRG1ZmZmZmZmY1csPKzMzMzMysRi2NLsBwMdi/KdCsWlpalnV3d2/Z+7y9vX1ZV1fXFo0sk6Wpra1tWWdn55aFw1yP+qxDS7u6utbqZWKlFdcj16FMcT1ab731nnvjjTc2bWSZLE19HYvMBsq/YzVIJEVHR0ef46r9Bexmznd0dBAR6n0uKfrbxmbOnMnkyZOZPn06EydO7Lc8zg+PvKS3bCv5sJgyZUoy23Mj8v3VoWZZv84PTb64HpU7FkH62/9g5QdyLIL01q/z9c/3dSwyGyh3BayzVA4yQ5WvVjPsdJ0fujyQ1PacWj619eV84/PVSG179vHI+ZTzZgPhhlUdpXaQGYp8NVLbiTrf2HyvlLbnlPKprS/n08hXKrXt2Y0q51POmw2UG1Z1ktpBZqjylUptJ+p8Y/OFUtqeU8mntr6cTydfidS2Z3/J53zKebNaNF3DStLZkhZKmi9prqS9+sl3SPpc/vhrkg7MH58paWSJ15wu6VFJIWnzasuY4kFmqPKVSG0n6nxj89VKefuv1zfvKa0v55srn9r27C/5nE85b1arpmpYSdobOAyYEBHjgAOBJyt9fUR8JSJuzZ+eCfTZsALuzKf9RLVlTPUgk0o+tZ2o843NVyu17XmoujOlsr6cb658atuzv+RzPuW82WBottutbwUsj4gVABGxvHeEpMXAtcCkfNDxEfFo4YslTQVuBLbO/2ZIWh4RkwpzETEnz1dVuJQPMinkU9uJOt/YfLVS256H8hqRFNaX882VT217Ti2f2vpyvrF5s8HSVGesgJuB7SQ9LOliSfsVjX85It4HXAicV2oiEXE+sASYVNyoqoakUyXNkjQLfDezclLbiTrf+Hw1UtuefeG98ynnU9ueU8untr6cb2zebDA1VcMqIl4FdgdOBf4KXCvpxILI1QX/9x6C8lwSEXtExB5AMgeN1PKp7USdTyNfqdS2ZzeqnE85D/6Sr5zU1pfzjc+bDaamalgBRMTKiJgZEV8FTgeOKhxd4vGQSOGgkVoeSG4n6nwa+Uqktj37bmbOp5zvldL2nFI+tfXlfBp5s8HUVA0rSTtL2qlg0HjeeoOJYwr+/7Gfyb0CbDB4petfagcZX3jvfMr51LZn383M+ZTzhVLanlPJg7/kc77vvNlgaqqGFbA+ME3SA5LmA7sAHQXj15N0D/BZ4Kx+pnUJcJOkGcUjJJ0h6SlgW2C+pB/XWvDUDjK+8N75lPOpbc++m5nzKeerlfL27y/5nE8tb1aNprorYETMBj5QJnJRRJxT9JqOgscnFjy+ALigxHzOB86vpayFUjvI+BoR51POp7Y9p5ZPbX0539h8tVLbnv0ln/Mp582q1WxnrJpOagcZN6qcTzkPvvC+nNTWl/ONz1cjte3ZxyPnU86bDURTnbEqJyJGN7oMxVI7yPjCe+dTzvdKaXtOKZ/a+nI+jXylUtue3ahyPuW82UApYshvnjcstba2Lu3p6RnV6HI0WktLy7Pd3d2rl0N7e/vSrq6utX652Jra2tqWdXZ2blk4rLW1dVlPT88WjSpTClpaWpZ1d3evXi6uQ1ZOcT3ysSgzYsSI53t6ejbrfe56ZKX0dSwyGyg3rMzMzMzMzGrka6zMzMzMzMxq5IaVmZmZmZlZjdywMjMzMzMzq5EbVmZmZmZmZjXqt2El6bOSNlTmMkn3STpoKApnZmZmZmbWDCo5Y3VyRLwMHAS8HTgJ+E5dS2VmZmZmZtZEKmlYKf9/CPDTiJhXMMzMzMzMzGytV0nDarakm8kaVr+VtAGwqr7FMjMzMzMzax79/kCwpHWA8cDjEfGipM2AbSJi/hCUz8zMzMzMLHkt/QUiYpWkZcAukvrNr63a29uXdnV1jWp0OVLR1ta2rLOzc8vW1talPT09a/VyaWlpWdbd3b1l4TBvL1ZKb93pfe46lCmuR65DVk5bW9uznZ2dq7cP16NMH/VoWVdX1xaNLFNKive/ZtWq5IzVd4FjgAeAlfngiIjD61y2piIpCpflzJkzmTx5MtOnT2fixIn9vn645SUREZIUHR0da+QXLVrE9OnTmTx5MmPGjOl3+s2c7+joICLecl1i8fZSLPX163z98r11p+B5TJkyJZntuVH54npUWIeaaf06PzT5SZMmrbG99HUsgubY/gcrX64elZLi+q1Xvnj/a1atSs5AHQHsHBEr6lyWYSOlnUSK+ZQOMkORr1Zq68v5xuaBpLbn1PKprS/n08hXKrXt2cejxubNalXJzSseB1rrXZDhIrWdRGr51A4yQ5GvRmrry/nG5nultD2nlE9tfTmfTr4SqW3PPh41Pm9Wq0rOWL0OzJX0O2D1WauIOKNupSpD0tnA8WTdElcBn4qIe8rkO4BXI+I/JX0NuD0ibpV0JnBJRLzex2uuBPYAuoE/5fPo7q9sKe4kUsqneJAZinylUltfzjc2Xyil7TmVPJDU+nK+ufKpbc8+HqWRN6tVJWesbgC+DtwFzC74G3KS9gYOAyZExDjgQODJSl8fEV+JiFvzp2cCI0tErwTeDewGtAOfrGT6Ke4kUsmnepAZinwlUltfzjc2X62Ut/96dmdKZX0531z51LZnH4/SyZvVqpK7Ak6TtC7wrnzQnys5e1MnWwHLe6/3iojlvSMkLQauBSblg46PiEcLXyxpKnAjsHX+N0PS8oiYVJiLiF8XvOZPwLaVFC7FnUQK+ZQPMinkU1tfzjc2X63UtuehvEYkhfXlfHPlU9ueU8untr5Sy5v1p98zVpImAo8AFwEXAw9L2re+xSrpZmA7SQ9LuljSfkXjX46I9wEXAueVmkhEnA8sASYVN6oKSWoFTgB+U2L8qZJmSZoFPsiXktJBI7V8auvL+cbnq5Ha9uwL751POZ/a9pxaPrX1lVrerBKVdAX8L+CgiNgvIvYFPgycW99i9S0iXgV2B04F/gpcK+nEgsjVBf/3HoRZXkx2TdYdJcpzSUTsERF7VDKx1HYSQ7UTSuWgkVo+tfXlfBr5SqW2PbtR5XzKefCXfOWktr5Sy5tVqpKGVWtE/Ln3SUQ8TAPvEhgRKyNiZkR8FTgdOKpwdInHVZP0VeDtwD/XMp1eqe0khnInlMJBI7U8kNT6cj6dfCVS2559NzPnU873Sml7Timf2vpKLW9WjUoaVrMkXSZpYv53KY27ecXOknYqGDQeeKLg+TEF///Yz+ReATYoMZ9Pkp2ZOy4iVg2stG9KbScxFPlqpHaQ8YX3zqecT2179t3MnE85Xyil7TmVPPhLPrPBVEnD6tPAQuAM4LPAA8Bp9SxUGesD0yQ9IGk+sAvQUTB+PUn3kJXzrH6mdQlwk6QZfYz7ETAK+KOkuZK+MtACp7aTGKp8pVI7yPjCe+dTzqe2PftuZs6nnK9Wytu/v+Qb+rzZQFRyV8AVwPfzv4aKiNnAB8pELoqIc4pe01Hw+MSCxxcAF5SYTyW/79Wv1HYSQ5mfNGlSv/nUDjK+RsT5lPOpbc+p5VNbX843Nl+t1LZnf8nX2LzZQJVsQEi6LiI+Kul++rheKf8dKSshtZ1EavnUDjJuVDmfcj617Tm1fGrry/nG56uR2vbs41Fj82a1KHdm5rP5/8OGoiC1iojRjS5Dr9R2EqnlUzvI+CDmfMp58N3MykltfTmfRr5SqW3PPh41Nm9WK0WUv3mepO9GxBf6G7a2a29vX9rV1TWq0eVIRVtb27LOzs4tW1tbl/b09KzVy2XEiBHP9/T0bFY4zNuLldJbd3qft7S0PLdy5cpNG1mmFLS0tCzr7u5evVxch6yc4nrkY1Gm+HjkevRW66677vMrVqzYrP+kWd8qaVjdFxETiobNd1dAMzMzMzOzTLlrrD4N/COwfX4Hvl4bAHfWu2BmZmZmZmbNouQZK0kbAZsA3wa+WDDqlYh4fgjKZmZmZmZm1hT67Qq4OihtAbT1Po+Iv9SrUGZmZmZmZs2k3x8IlvQRSY8Ai4DbgMXATXUul5mZmZmZWdPot2EFfAN4P/BwRIwBDsDXWJmZmZmZma1WScOqOyKeA9aRtE5EzADG17dYZmZmZmZmzaPcDwT3elHS+sDtwJWSngV66lssMzMzMzOz5lHJ71i9DegkO7v1MWAj4Mr8LJaZmZmZmdlar2zDStII4LcRceDQFcnMzMzMzKy5lO0KGBErJb0uaaOIeGmoCtWM2tvbl3Z1dY1qdDksPW1tbc92dna+ZdtobW1d2tPTs1ZvLy0tLcu6u7u3LBzmemSltLW1Levs7Fy9vbgOZYrrkeuQlVN8PHI96vtYZDZQlXQFvI7sroC3AK/1Do+IM+pbtOYiKSKCmTNnMnnyZKZPn87EiRP7fZ3zwz8/adIkIkKFwyVFR0fHGvlFixYxffp0Jk+ezJgxY/qdfjPnOzo6+lwu5fZJKa5f54cmL+kt24ukmDJlSjLbc6PyxfWosA410/p1vjHHo1LHImiO7X8w8n0di8wGqpKbV/wq/7N+pLgTdb7x+UqlcpAZqny1Ul2/zjcmDyS1PaeWT219OZ9GvlKpbc+pHY/MSun3dusRMQ24Drg7Iqb1/tW/aM0nxZ2o843PVyK1g8xQ5KuR8vp1fujzvVLanlPKp7a+nE8nX4nUtmc3qqyZ9NuwkvQRYC7wm/z5eEk31LlcTSnFnajz6edTO8gMVb5Sqa0v5xubL5TS9pxKHvwln/MDz6e2Paf2JZ9Zfyr5geAO4H3AiwARMRdo2FcAks6WtFDSfElzJe3VT75D0ufyx1+TdGD++ExJI0u85jJJ8/J5XJ//jle/UtgpOt9c+RQPMkOVr0Rq68v5xuarlfL2X89v3lNZX843Vz617Tm1L/nMKlFJw6qnjzsClr/jRZ1I2hs4DJgQEeOAA4EnK319RHwlIm7Nn54J9NmwAs6KiPfk8/gLcPrAS/2m1Haizjc2n+pBJpV8auvL+cbmq5Xa9jyU3ZlSWF/ON1c+te05tS/5zCpVyc0rFkg6HhghaSfgDOCu+harpK2A5RGxAiAilveOkLQYuBaYlA86PiIeLXyxpKnAjcDW+d8MScsjYlJhLiJezvMC2hmEhmRqO1HnG5sHX3hfTmrry/nG56uR2vac2jUiKa5f5xuXT217Ti1vVo1Kzlh9BtgVWAFcBbwEfLaehSrjZmA7SQ9LuljSfkXjX46I9wEXAueVmkhEnA8sASYVN6p6SfopsBR4N3BBicypkmZJmlWu0KntRJ1vbL5XKgeN1PKprS/n08hXKrXtObUPfamuX+cbkwd/yWc2mCppWB0aEWdHxJ75378Bh9e7YH2JiFeB3YFTgb8C10o6sSBydcH/vWuc10lkZ7UeBI4pkbkkIvaIiD1KTSe1najzjc0XSuGgkVoeSGp9OZ9OvhKpbc+pXXif8vp1fujzvVLanlPKmw1EJQ2rL1U4bEhExMqImBkRXyW79umowtElHg94XmTdC4/qL9uX1Haizjc2X63UDjK+8N75lPOpbc+pXXif2vpyvrH5Qiltz6nkzQaqZMNK0sGSLgC2kXR+wd9UoGfISvjWMu2cX+fVazzwRMHzYwr+/7Gfyb0CbNDHPCRpx97HwEeAh6ota2o7Uecbm69WagcZX3jvfMr51Lbn1C68T219Od/YfLVS3v7dqLLUlLt5xRJgFlm3v9kFw18BzqpnocpYH7hA0sZkjbtHyboF9lpP0j1kDcbj+pnWJcBNkp4pus5KwDRJG+aP5wGfrqaQqe1EnW9svlqpHWRSO4iltn6db2w+te05tXxq68v5xuarldr2nNrxyKxYyYZVRMwD5km6MiIacoaqWETMBj5QJnJRRJxT9JqOgscnFjy+gD5uShERq4C/HWgZU9uJOt/4fDVSO8ikdhBLcf0637h8attzavnU1pfzjc9XI7XtObXjkVlfSjasJF0XER8F5kha43ql/DeerECKO1HnG5+vVGoHmdQOYqmuX+cbkwffzayc1NaX82nkK5Xa9pza8cislHJdAXtvqX7YUBSkVhExutFlSHEn6nzj85MmTeo3n9pBxnczcz7lfK+UtueU8qmtL+fTyft4ZFZfiqj55nkGrLfees+98cYbmza6HJaetra2ZZ2dnVsWDmttbV3a09MzqlFlSkFLS8uz3d3db1kG7e3tS7u6utbq5WJ9K65Hra2ty3p6erZoZJlS0NLSsqy7u3v1cmlvb1/W1dW11i8X61sf9WitPxaNGDHi+Z6ens0aXQ4bHtywMjMzMzMzq1Elv2NlZmZmZmZmZZT7HavPSdpuKAtjZmZmZmbWjMqdsdoGuEvS7ZI+LWnzoSqUmZmZmZlZMyl7jZUkAfsCxwJ/T/ZjuVcDv4iIV4akhGZmZmZmZomr+OYVkkYABwLfAXaOiJH1LJiZmZmZmVmzKPc7VqtJ2o3srNUxwHPAl+tZKDMzMzMzs2ZSsmElaSfgOLIG1UrgGuCgiHh8iMpmZmZmZmbWFEp2BZT0ONn1VNdExP1DWiozMzMzM7MmUq5htRMwKiL+UDR8H2BJRDw2BOVrGu3t7Uu7urrW6l8vN6tGW1vbss7Ozi0Lh7kemVWnuB65DplVp69jkdlAlWtY3Qh8OSLmFw3fA/hqRHxkCMrXNCRFpTcCMTOQRESoaJjrkVkViuuR65BZdfo6FpkNVLnfsRpd3KgCiIhZwOi6lcjMzMzMzKzJlGtYtZUZ1z7YBTEzMzMzM2tW5RpW90o6pXigpE8As+tXJDMzMzMzs+ZS7hqrUcAvgDd4syG1B7AucGRELC07YWlL4DxgT2AFsBg4M5/ejRExtubSrznPDuDViPjPMpmp+fyvr3Cao6mgvO7XblYdX2NlVjtfY2VWG19jZYOp5BmriFgWER8AziFrFC0GzomIvStoVImsUTYzInaIiF3IflTYdyqyAVm6dCnHHnssO+ywA7vssguHHHIIDz/8MIsXL2bs2EFvowOwYsUKjjnmGHbccUf22msvFi9eXJf5mA2FRtSh22+/nQkTJtDS0sL111f0XZZZ0hpRj77//e+zyy67MG7cOA444ACeeOKJuszHzGpXrisgABExIyIuyP9+X+F0JwHdEfGjgunMjYg7CkOSRku6Q9J9+d8H8uFbSbpd0lxJCyTtI2mEpKn58/slnVWuAJJOkXSvpHmSfi5pZMHoA/P5PizpsDw/QtL38tfMl/SpCt+r1VlEcOSRRzJx4kQee+wxHnjgAb71rW+xbNmyus73sssuY5NNNuHRRx/lrLPO4gtf+EJd52dWL42qQ+94xzuYOnUqxx9/fF3nYzYUGlWP3vve9zJr1izmz5/P0Ucfzec///m6zs/MBq7fhtUAjaWy67CeBT4UEROAY4Dz8+HHA7+NiPHAe4C5wHhgm4gYGxG7AT/tZ9r/ExF7RsR7gAeBTxSMGw3sBxwK/EhSWz7+pYjYk6z74imSxpSbgaRTJc2SNKuC92oDNGPGDFpbWznttNNWDxs/fjz77LPPW3KLFy9mn332YcKECUyYMIG77roLgGeeeYZ9992X8ePHM3bsWO644w5WrlzJiSeeyNixY9ltt90499xz15jvL3/5S6ZMmQLA0Ucfze9+9zvcxcaaUaPq0OjRoxk3bhzrrFOvQ43Z0GlUPZo0aRIjR2bfDb///e/nqaeequO7NLNatDR4/q3AhZLGAyuBd+XD7wV+IqkV+N+ImCvpcWB7SRcAvwJu7mfaYyV9A9gYWB/4bcG46yJiFfBIPt13AwcB4yQdnWc2AnYCHi41g4i4BLgEsn7tlb1lq9aCBQvYfffd+81tscUW3HLLLbS1tfHII49w3HHHMWvWLK666io+/OEPc/bZZ7Ny5Upef/115s6dy9NPP82CBQsAePHFF9eY3tNPP812220HQEtLCxtttBHPPfccm2+++aC+P7N6a1QdMhtOUqhHl112GQcffPBgvB0zq4N6NawWAkf3m4KzgGVkZ6XWAboAIuJ2SfuSnVH6maTvRcTlkt4DfBj4J+CjwMllpj0VOCIi5kk6EZhYMK64ERSAgM9ERGEDrPfmFdYEuru7Of3005k7dy4jRozg4YezNvGee+7JySefTHd3N0cccQTjx49n++235/HHH+czn/kMhx56KAcddNAa0+vr7FR2+aDZ8DTYdchsbVSvenTFFVcwa9YsbrvttqF6K2ZWpXr1z/g9sF7h7dol7Slpv6LcRsAz+dmjE4ARefadwLMRcSlwGTBB0ubAOhHxc+DfgQn9lGED4Jn8rNfHisZNlrSOpB2A7YE/k53R+nSeR9K7JL2t6ndug27XXXdl9uz+e5aee+65jBo1innz5jFr1izeeOMNAPbdd19uv/12ttlmG0444QQuv/xyNtlkE+bNm8fEiRO56KKL+OQnP7nG9LbddluefPJJAHp6enjppZfYdNNNB/fNmQ2BRtUhs+GkkfXo1ltv5Zvf/CY33HAD66233qC+LzMbPHVpWOX3ej0S+JCkxyQtBDqAJUXRi4Epku4m6wb4Wj58IjBX0hzgKOAHwDbATElzyc5GfamfYvw7cA9wC/BQ0bg/A7cBNwGnRUQX8GPgAeA+SQuA/6bxXSUN2H///VmxYgWXXnrp6mH33nvvGt/avfTSS2y11Vass846/OxnP2PlypUAPPHEE2yxxRaccsopfOITn+C+++5j+fLlrFq1iqOOOoqvf/3r3HfffWvM9/DDD2fatGkAXH/99ey///4+Y2VNqVF1yGw4aVQ9mjNnDp/61Ke44YYb2GKLLer7Js2sJiV/x8qq498Oqa8lS5Zw5plnMnv2bNra2hg9ejTnnXcera2tHHbYYSxYsIBHHnmEo446ipEjRzJp0iQuuOACXn31VaZNm8b3vvc9WltbWX/99bn88st5+eWXOemkk1i1ahUA3/72t9fot97V1cUJJ5zAnDlz2HTTTbnmmmvYfvvtG/H2hyX/jtXQakQduvfeeznyyCN54YUXaGtrY8stt2ThwoWNePvDln/Hamg1oh4deOCB3H///Wy11VZAdrfNG264Ycjf+3Dl37GyweSG1SDxwcysOm5YmdXODSuz2rhhZYPJ98A1MzMzMzOrka8hGiRtbW3LJI1qdDnMmkVbW9sav6rpemRWneJ65DpkVp2+jkVmA+WugGZmZmZmZjVyV0AzMzMzM7MauWFlZmZmZmZWIzeszMzMzMzMauSGlZmZmZmZWY3csDIzMzMzM6uRG1ZmZmZmZmY1csPKzMzMzMysRm5YmZmZmZmZ1cgNKzMzMzMzsxq5YTVIJJ3a6DIMJ16eg8vLc/B4WQ4uL8/B5eU5uLw8B5eXpw13blgNHu8sBpeX5+Dy8hw8XpaDy8tzcHl5Di4vz8Hl5WnDmhtWZmZmZmZmNXLDyszMzMzMrEZuWA2eSxpdgGHGy3NweXkOHi/LweXlObi8PAeXl+fg8vK0YU0R0egymJmZmZmZNTWfsTIzMzMzM6uRG1ZmZmZmZmY1csOqRpL+TtKfJT0q6YuNLk+zkbSdpBmSHpS0UNJn8+GbSrpF0iP5/00aXdZmImmEpDmSbsyfe3kOkKSNJV0v6aF8O93by3PgJJ2V1/UFkq6W1OblWTlJP5H0rKQFBcNKLj9JX8qPT3+W9OHGlDpNJZbl9/K6Pl/SLyRtXDDOy7KMvpZnwbjPSQpJmxcM8/K0YccNqxpIGgFcBBwM7AIcJ2mXxpaq6fQA/xIRfwO8H/infBl+EfhdROwE/C5/bpX7LPBgwXMvz4H7AfCbiHg38B6y5erlOQCStgHOAPaIiLHACOBYvDyrMRX4u6JhfS6/fF96LLBr/pqL8+OWZaay5rK8BRgbEeOAh4EvgZdlhaay5vJE0nbAh4C/FAzz8rRhyQ2r2rwPeDQiHo+IN4BrgL9vcJmaSkQ8ExH35Y9fIfvQug3ZcpyWx6YBRzSkgE1I0rbAocCPCwZ7eQ6ApA2BfYHLACLijYh4ES/PWrQA7ZJagJHAErw8KxYRtwPPFw0utfz+HrgmIlZExCLgUbLjltH3soyImyOiJ396N7Bt/tjLsh8ltk2Ac4HPA4V3S/PytGHJDavabAM8WfD8qXyYDYCk0cB7gXuAURHxDGSNL2CLBhat2ZxHdhBbVTDMy3Ngtgf+Cvw071r5Y0lvw8tzQCLiaeA/yb65fgZ4KSJuxsuzVqWWn49RtTkZuCl/7GU5AJIOB56OiHlFo7w8bVhyw6o26mOY718/AJLWB34OnBkRLze6PM1K0mHAsxExu9FlGSZagAnADyPivcBruJvagOXX/vw9MAbYGnibpI83tlTDmo9RAyTpbLKu6lf2Duoj5mVZhqSRwNnAV/oa3ccwL09rem5Y1eYpYLuC59uSdWuxKkhqJWtUXRkR/5MPXiZpq3z8VsCzjSpfk/lb4HBJi8m6pu4v6Qq8PAfqKeCpiLgnf349WUPLy3NgDgQWRcRfI6Ib+B/gA3h51qrU8vMxagAkTQEOAz4Wb/7Yp5dl9XYg+xJlXn5M2ha4T9KWeHnaMOWGVW3uBXaSNEbSumQXYt7Q4DI1FUkiu37lwYj4fsGoG4Ap+eMpwC+HumzNKCK+FBHbRsRosu3x9xHxcbw8ByQilgJPSto5H3QA8ABengP1F+D9kkbmdf8AsusqvTxrU2r53QAcK2k9SWOAnYA/NaB8TUPS3wFfAA6PiNcLRnlZViki7o+ILSJidH5MegqYkO9XvTxtWGppdAGaWUT0SDod+C3Z3a1+EhELG1ysZvO3wAnA/ZLm5sO+DHwHuE7SJ8g+jE1uTPGGDS/PgfsMcGX+5cnjwElkX0p5eVYpIu6RdD1wH1k3qznAJcD6eHlWRNLVwERgc0lPAV+lRP2OiIWSriP7MqAH+KeIWNmQgieoxLL8ErAecEvW9ufuiDjNy7J/fS3PiLisr6yXpw1XevMst5mZmZmZmQ2EuwKamZmZmZnVyA0rMzMzMzOzGrlhZWZmZmZmViM3rMzMzMzMzGrkhpWZmZmZmVmN3LAyMxsGJK2UNFfSQknzJP2zpLL7eEmjJR0/BGX7saRd+skc0V/GzMwsZW5YmZkND50RMT4idgU+BBxC9rs85YwG6t6wiohPRsQD/cSOANywMjOzpuWGlZnZMBMRzwKnAqcrM1rSHZLuy/8+kEe/A+yTn+k6q0xutTzzkKRpkuZLul7SyHzcAZLmSLpf0k8krZcPnylpj/zxq5K+mZ9Vu1vSqHw+hwPfy8uyg6QzJD2Qz+OaoVhuZmZmtfAPBJuZDQOSXo2I9YuGvQC8G3gFWBURXZJ2Aq6OiD0kTQQ+FxGH5fmRfeWKpjkaWAR8MCLulPQT4AHgQuAR4ICIeFjS5cB9EXGepJn5fGZJCuDwiPg/Sf8BvBwR35A0FbgxIq7P57MEGBMRKyRtHBEvDvpCMzMzG0Q+Y2VmNnwp/98KXCrpfmA6pbvcVZp7MiLuzB9fAXwQ2BlYFBEP58OnAfv28do3gBvzx7PJuiP2ZT5wpaSPAz0lMmZmZslww8rMbBiStD2wEngWOAtYBrwH2ANYt8TLKs0Vd3UI3mzE9ac73uwqsRJoKZE7FLgI2B2YLalUzszMLAluWJmZDTOS3g78CLgwb8RsBDwTEauAE4ARefQVYIOCl5bKFXuHpL3zx8cBfwAeAkZL2jEffgJwWxXFXl2W/G6G20XEDODzwMbA+qVfamZm1nhuWJmZDQ/tvbdbB24FbgbOycddDEyRdDfwLuC1fPh8oCe/kcRZZXLFHsxz84FNgR9GRBdwEjA970q4iqxxV6lrgH+VNAfYCbgin84c4FxfY2VmZqnzzSvMzKxi+c0rboyIsY0ui5mZWUp8xsrMzMzMzKxGPmNlZmZmZmZWI5+xMjMzMzMzq5EbVmZmZmZmZjVyw8rMzMzMzKxGbliZmZmZmZnVyA0rMzMzMzOzGv1/EDa/80jDUlkAAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 864x360 with 2 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_stratified_cross_validation()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> It is usually a good idea to use stratified k-fold cross-validation instead of k-fold cross-validation to evaluate a classifier, because it results in more reliable estimates of generalization performance. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For regression, scikit-learn uses the standard k-fold cross-validation by default. It would be possible to also try to make each fold representative of the different values the regression target has, but this is not a commonly used strategy and would be surprising to most users"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### More control over cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Scikit-learn allows for much finer control over what happens during the splitting of the data by providing a crossvalidation splitter as the cv parameter. <br><br>\n",
    "\n",
    "- For most use cases, the defaults of k-fold crossvalidation for regression and stratified k-fold for classification work well, but there are some cases where you might want to use a different strategy\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import KFold \n",
    "kfold = KFold(n_splits=5)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Then, we can pass the kfold splitter object as the cv parameter to cross_val_score:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[1.         1.         0.86666667 0.93333333 0.83333333]\n"
     ]
    }
   ],
   "source": [
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "      cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This way, we can verify that it is indeed a really bad idea to use three-fold (nonstratified) cross-validation on the iris dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0. 0. 0.]\n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3)\n",
    "print(\"Cross-validation scores:\\n{}\".format(\n",
    "    cross_val_score(logreg, iris.data, iris.target, cv=kfold)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Iris labels:\n",
      "[0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0 0\n",
      " 0 0 0 0 0 0 0 0 0 0 0 0 0 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1\n",
      " 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 1 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2 2\n",
      " 2 2]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.datasets import load_iris\n",
    "iris = load_iris()\n",
    "print(\"Iris labels:\\n{}\".format(iris.target))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Remember: each fold corresponds to one of the classes in the iris dataset, and so nothing can be learned\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Another way to resolve this problem is to shuffle the data instead of stratifying the folds, to remove the ordering of the samples by label. We can do that by setting the shuffle parameter of KFold to True. <br><br>\n",
    "> If we shuffle the data, we also need to fix the random_state to get a reproducible shuffling. Otherwise, each run of cross_val_score would yield a different result, as each time a different split would be used (this might not be a problem, but can be surprising). Shuffling the data before splitting it yields a much better result:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      " [0.98 0.96 0.96] \n"
     ]
    }
   ],
   "source": [
    "kfold = KFold(n_splits=3, shuffle=True, random_state=0)\n",
    "score = cross_val_score(logreg, iris.data, iris.target, cv=kfold)\n",
    "print(f\"Cross-validation scores:\\n {score} \")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Leave-one-out cross-validation\n",
    "\n",
    "- LOOCV Extreme version of k-fold cross-validation — To estimate the performance of machine learning algorithms <br><br>\n",
    "- LOOCV is a cross validation approach that utilizes each individual unit as a “test” set. <br><br>\n",
    "- It is a specific type of k-fold cross validation, where the number of folds, k, is equal to the number of participants in your dataset."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/LOOUT.jpeg)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Number of cv iterations:  150\n",
      "Mean accuracy: 0.97\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import LeaveOneOut\n",
    "loo = LeaveOneOut()\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=loo)\n",
    "print(\"Number of cv iterations: \", len(scores))\n",
    "print(\"Mean accuracy: {:.2f}\".format(scores.mean()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Shuffle-split cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Very flexible strategy for cross-validation is shu(e-split cross-validation. In shuffle-split cross-validation, each split samples train_size many points for the training set and test_size many (disjoint) point for the test set. This splitting is repeated n_iter times. <br><br>\n",
    "- Figure 5-3 illustrates running four iterations of splitting a dataset consisting of 10 points, with a training set of 5 points and test sets of 2 points each (you can use integers for train_size and test_size to use absolute sizes for these sets, or floating-point numbers to use fractions of the whole dataset):"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_shuffle_split()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0.947 0.987 0.973 0.96  0.933 0.96  0.987 0.947 0.947 0.933]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ShuffleSplit\n",
    "shuffle_split = ShuffleSplit(test_size=.5, train_size=.5, n_splits=10)\n",
    "scores = cross_val_score(logreg, iris.data, iris.target, cv=shuffle_split)\n",
    "print(\"Cross-validation scores:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Cross-validation with groups\n",
    "\n",
    "- Another very common setting for cross-validation is when there are groups in the data that are highly related.  <br><br>\n",
    "- Say you want to build a system to recognize emotions from pictures of faces, and you collect a dataset of pictures of 100 people where each person is captured multiple times, showing various emotions. The goal is to build a classifier that can correctly identify emotions of people not in the dataset.  <br><br>\n",
    "- You could use the default stratified cross-validation to measure the performance of a classifier here. <br><br>\n",
    "- However, it is likely that pictures of the same person will be in both the training and the test set. <br><br>\n",
    "- To accurately evaluate the generalization to new faces, we must therefore ensure that the training and test sets contain images of different people\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To achieve this, we can use GroupKFold, which takes an array of groups as argument that we can use to indicate which person is in the image. The groups array here indicates groups in the data that should not be split when creating the training and test sets\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> This example of groups in the data is common in medical applications, where you might have multiple samples from the same patient, but are interested in generalizing to new patients. Similarly, in speech recognition, you might have multiple recordings of the same speaker in your dataset, but are interested in recognizing speech of new speakers."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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ry4YPHz5y8uTJRf+R5GyQzeyN6OKlZrY6fQEuLbDdx4CBkl6WdIekj2Tcvt3MJgA/BG7Lk2E6sB6YZGY55yBI6gb8M/BogVwFhfZmGVp9qULLH2J9KULLDwSVp7W+WCGOJ3gz11H16ULIk/T6UiU9f2jien9/9dVXq5555pmXf/rTn66tra3dPW/evLoVK1Ysv/7669d99atfzbpjcuXKlVVPPfXUy3/5y19W3HLLLYfV19crs2bFihXVP/rRj9asXLly2euvv175+OOP99i5c6cuv/zyIx955JFXFixY8NKbb76ZdYrJ7bff3ufSSy/dWFdXt3zJkiUrBg8e3PDCCy9U3X///b3mz59fV1dXt7ysrMx+/OMfH3LHHXesq6ysbKmrq1v+4IMPFv1xWDFzWz4KXJux7vQs695lZm9LGgd8GJgE3CfpOjObGZXck/bz+8WGzeMOUnOW/5TtRkkXAxcX2khob5Yh1pcitPzgzVxH1rcKJU96fdLn9IfQTCS9vlSh5Q+tvlRJzx+aOHd+ffrTn/5bRUWqXdy6dWv52WefPXjVqlVVkqyxsXGvxhfg1FNPfau6utqqq6ubevXq1bh27dqKo48+eo85GqNHj36ndd2oUaN2vvrqq9179uzZPHDgwPrhw4c3AEydOnXrjBkz+mRu/4QTTnjnlltuOXTt2rXdp06d+rfRo0fXP/rooz2XLl1aU1tbOwJg9+7dZX379m1q6++dbw7yFyW9CAzLmH/8GpB3DjKAmTWb2ZNmdj3wZeDM9JtzXC6ZpOuBPsBVebLcaWbjzWx8rpoQ3yxDyuPNXOfVFyPU8QSf098R9aUKrfkIrb5UoeUPrb5USc8fmrhff3r06NHSevnaa689/CMf+ciOV155Zdnvf//7lQ0NDVn7yMrKynd7u/LycpqamvZqpLPVFHO0IYBLLrlk6wMPPLCyurq65fTTTx/64IMP9jQzTZky5c3WedOrVq1a+r3vfW99Sb9smnxzkH9Faq7xg+w593icmf1Tvo1KGiZpSNqqMcDqtOtnp/18rkDGHUDPHI9zEfAPwDlm1pKtphihvlmGksebOa9vj9Dyh1ZfqtCajxDrSxFi/tDqSxFa/qQL7fVn+/bt5QMGDGgA+MlPftK7o7dfW1u7e82aNZUvvfRSd4D77rsv6/Hrly9f3n3EiBH13/jGNzadeuqpby1atKj6tNNO2/7QQw8d3Hrkj40bN5a//PLL3QEqKios2zSPfHJOsTCzbcA24BwASX2BKqCHpB5m9nqe7fYAbpd0ENAErGTPKQ6Vkp4n1aCfUyDjncAjkt7IMg/5x6Qa7+ckAfzWzG4ssL09hPxmGUKepNeXKrT8odWXKrT8IdaXIrTmI9T6YoWWH/wLmh1Zn3SHHnpo06RJkyqAoqaNpctWf/jhh7d5ukGra6+9dsNFF100ePr06f0//OEPb2/v9jL16NHDvve9760+7bTThvTq1atp7Nix72Sr+8UvftFrzpw5h1RUVFifPn0a//M//3N9v379mr/xjW+sO/nkk4e2tLTQrVs3mz59+utDhw5tOO+88zaPGDFi5LHHHruz2HnIxZwo5BOkjoF8GLAJOBJYYWajSvu1393eKmC8mcVyMOz0EwaE9GbZlhMbxJE/30ktQhrPbFkLjWlc+fOdfCWk8XzyySeLOvlKV+bJVZ/t3z7E8ZwyJftJgrKdLCLu5iPbSS1qamqCaYbS6zNPFpFtTOMeT8g+phdccEHw49maNX1MQxhPyH5Sk6TwE4WkbNu2rezAAw9saWlp4fzzzz9iyJAhu6+//vpNnfV4uU4UUkyDvBg4CXjCzMZKmkRqSkPBL73l2N4qYmyQAz41bmJPixxqVj99d8dL6t9pqOMJyT3VdEVFxZvNzc1Bnr47qadwDvX03Qk71fReWZPCG+SUG264oe8999zTu7GxUaNGjdo5e/bs1T179mzzNNpC2tMgzzez8VGjPNbMWiTNiw7T5pxzzjnn2skb5Hi0+VTTwFuSegBPA7MlbSI1r9g555xzzrl9Tr6jWLT6JLATuJLUiThepfCZ9JxzzjnnnEukvHuQJZUDD5jZKUALMKtLUjnnnHPOOReTvHuQzawZ2CnpwC7K45xzzjnnXKyKmYO8G3hR0uPAu8ejM7PLOi2Vc84559x+rH///rUbN24spk8rSr9+/Zo2bNiwONftGzZsKJ84ceIwgC1btnQrKyuzXr16NQEsWrRoRVVVVd6jOjz00EM9KysrWz760Y9mPXZxsbZs2VI+Y8aMXtddd93m9mynvYoZ+IejxTnnnHPOdYGNGzdWtB5ruiOOGz1t2rS8PV///v2b6+rqlgNcddVVh/Xo0aP5xhtv3Fhs3j/+8Y89e/To0dzeBvnNN98sv+uuu/oG3yCb2SxJ1cARZvZSF2TqVEk5dmPAx8Pc1NjYuMf4JWVMKysr32xoaAjuuK1JOrZ0Uo4vnCVnkOMJycmalJyQ6ON1h5pzj/GEoLPu9XqadHGecfBPf/pTzVVXXTVw586dZQcffHDT7NmzVx155JGNN910U9+77767T3l5uQ0dOnT3rbfeuvbnP/95n7KyMvv1r399yG233fb6aaed9nbrdh5++OEeV1999RGQOpHTs88+W3fwwQe3fPOb3+z3u9/9rldDQ4M+/vGPv/X9739//dVXXz1gzZo1lcOHDx/5kY98ZPtPfvKTte36JdqoYIMcnUnvFqA7MFjSGOBGM5vcydk6RVNTU79Az6i0xwtNU1NT30DPqLRXM5Q5piGMJ+w9pg0NDb0KHfcbuv4McJL2epPZvXt3v0DP+LhH1t27d/cN9IyPmTmDHM9CWePIk+eMj1lzhjae0Rkf93idyjemMZ/xca8xDfGMj5nj2Zo125jG/feQ7fU0yeJsjs2Myy677IiHH3545WGHHdb005/+9OBrrrnm8Dlz5qyaPn16/9WrV79YXV1tW7ZsKe/du3fz+eefvznXXudbb721//Tp01efeuqp72zbtq2spqam5be//e0BK1eurFqyZMkKM+OUU0455pFHHulx6623rj3jjDOqW/dmx6WYKRbTgAnAkwBmtkhSwVGX9HXgXKCZ1BEwvmBmz+epnwa8bWa3SLoReNrMnpB0BXCnme3Mcp+7gPGAgJeBC83s7cy6fEJp5rIJIU/S60sV94t76PWlCi1/iPWlCDF/iPXFCi0/EFSefWE8ky7O98f6+vqyV155pfqkk04aCtDS0kKfPn0aAYYNG7brU5/61ODJkye/dd55571VaFvvf//7377mmmsGfuYzn9l6zjnn/O3oo49uefTRRw94+umnDxg5cuRIgJ07d5bV1dVVHXXUUQ3tDt8BijkOcpOZbctYl3eXkaQTgDOA483sOOAUYE2xoczsW2b2RHT1CqAmR+mVZlYbPcbrwJeLfQxIfjMXWv7Q6ksV2ot7iPWlCDF/iPXFCi0/hNnMzZkzp3DwLs5TSjMXSp59ZTyTLs73RzPjmGOO2VVXV7e8rq5u+csvv7z8mWeeeQVg7ty5r3zpS1/avGDBgvfU1taObGxszLut//iP/9gwY8aM1bt27Sr7wAc+MGLhwoVVZsYVV1zxRuv2X3/99aVXXnllMGcSLKZBXirpXKBc0hBJtwPPFrjPocAWM6sHMLMtZrYeQNIqSd+RNC9ajsm8s6SZks6SdBlwGDBX0tzMOjPbHtULqKZA454u6c1caPlDrC9FiC/uIeXxZq7z6osR4nhCmM1cEuvThZAn6fX7kjj7jcrKypatW7dWPPHEE+8BqK+v1/z586uam5t59dVXu3/iE5/Ycccdd6zdsWNH+bZt28p79uzZvGPHjvJs21q2bFnlhAkTdn3729/eMHr06HeWLl1adfrpp2//xS9+0Xvbtm1l0e/Rbd26dRUHHnhg8zvvvFNMf9qpipli8RXg60A98Cvgf4F/L3Cfx4BvSXoZeAK4z8yeSrt9u5lNkHQ+cBupvc17MbPpkq4CJplZ1v9VSLob+BiwHLi6iN8n8c1caPmhcz8Gamt9sUJ9cQ8lT3r9pEmTgsqT9PFMYn26EPIkvb5UoeUPrX5f0q9fv6ZCR55IN2vWrILbK+Xxy8rKuPfee1+97LLLjtixY0d5c3OzvvjFL24cPXp0/bnnnjt4x44d5WamL3zhCxt79+7dfOaZZ7511llnHf3II48clPklve9+97t9n3322QPKysps6NChu84666xt1dXVtmzZsqr3vve9wwFqampaZs+e/dqoUaPqx40b9/aQIUNGnXTSSdvi+pKeCn3BRtIUM5tTaF2W+5UDHwYmAV8ArjOzmZJWASeZ2V8ldQM2mNkhGXOQZwIPmdn9Uf34XA1y2mPdDvzFzO7OcvvFwMXR1XE1NTXBNXM7d+7EzJSW2TK/TNiVeXLVT5s2bY+crVkD/ULhXmOa+fcewou7pKxjGugXCvOOaQjjGeUq+G/flXny1efLGsp4FsoZR5589bmyhjSe+XLGlafY8WzNGugXCvfKmhSLFy9eVVtbG8wUg/3F4sWLe9fW1g7KXF/MLuyvFbluD2bWbGZPmtn1pOYGn5l+c47LbRKd8e++jMdIv/1OMxtvZuOh8+b0tKe+GHE3x/mEkKfU+tDeLEsVWv7Q6ksVWv7Q6ksVWv4Q60sRWn4Ic9qScx0lZ4Ms6fRovvHhkqanLTOBvLvpJQ2TNCRt1Rhgddr1s9N+Plcg4w6gZ5bHUOv85WgO8ieAugLbApLZzIVWX6rQ8of4ZlOK0PKHVl+q0PKHWF+K0PJDspu5EMcTwpy25FxHyTe3ZT0wH5gMLEhbvwO4ssB2ewC3SzqIVDO9kr9PcQColPQ8qQb9nALbuhN4RNIbZjYpbb2AWZIOiC4vBr5YYFtFCa2ZC62+VKHlD/XNplih5Q+xvhSh5Qdv5jqyvlUoedLrkzynH5I5Bz1wLS0tLSorK2v3J+uuOC0tLSJ1KOK95GyQzWwxsFjSbDMraWK3mS0APpCn5EdmdkPGfaalXb4w7fLtpOYXZz5GC/DBUnIVI7RmLrT6UoWWP7QXa2/mOqe+WCGOJ3gz11H16ULIk/T6UiU9fwyWbt68eWSfPn22eZPc+VpaWrR58+YDgaXZbs/5JT1Jvzazz0h6kSzzhKNjD5esmC/ddaaknBY51Jzl5eVbm5qaDklfF2rWzDEN+LTIiT2Na1JO3x3qeIKfvrszJCVrUnNCsrImxYIFC/pWVFTMAI6luO+IufZpAZY2NTVdNG7cuE2ZN+ZrkA81szckHZntdjNbnW29c84555xzSVbwMG/OOeecc87tT3wXvnPOOeecc2m8QXbOOeeccy5NvuMgXyNpYFeGcc4555xzLm759iAfDjwr6WlJX5TUu6tCOeecc845F5e8X9KLzlB3IjAV+CSpk3HcA/zOzHZ0SULnnHPOOee6kpkVtQDlwD8AC4Gdxd4vtKWqqmoDqeM6B7VUVVVtSGLOJGVNSs4kZfWc+0/WpORMUtak5kxaVl98actS1GHeJI0mtRf5bOBN4B4zu63gHQMkyYr5nbuaJMxMadcTkTNal4isSckZrUtEVs/ZfknJmpSckJysSc0ZrUtMVufaIt+X9IZI+pak5cCvgJ3AqWb2vlKaY0n9JP1K0l8lLZD0nKRPtT96/B599FGGDRvGMcccw8033xx3nLySkjUpOT/3uc/Rt29fjj322LijFJSUrEnJCcnJumbNGiZNmsSIESMYNWoUP/jBD+KOlFVScgLs3r2bCRMmUFtby6hRo7j++uvjjpSV53SunXLtWgb+CnwbGN3W3dOAgOeAS9LWHQl8JaOuoqt2mad+5fZramqyo446yl599VWrr6+34447zpYtW9bm7UW5OjynWcdmzczZkVmTNKZPPfWULViwwEaNGtXubXXmmJp1btb9MadZcrKuX7/eFixYYGZm27dvtyFDhnTYcz/UnGadm7WlpcV27NhhZmYNDQ02YcIEe+6559q0raTmtA7M2pE5zbJn9cWXtiz5jmLxD8AjZvZi+kpJH5Z0dJH990lAg5n9uHWFma02s9slXShpjqTfA49J6iXp/0laIunPko6LHm+apGvSHn+ppEHRUidpVnSf+yXVFJmr3ebNm8cxxxzDUUcdRffu3Zk6dSoPPPBAVz18SZKSNSk5AU488UR69eoVd4yiJCVrUnJCcrIeeuihHH/88QD07NmTESNGsG7duphT7S0pOSH1EX6PHj0AaGxspLGxkdT32cPiOZ1rn3wN8veB7VnW7wJuK3L7o4AX8tx+AnCBmZ0E3AAsNLPjgH8Dfl7E9ocBd0b32Q5cWmSudlu3bh0DB/79MNEDBgwI9gU9KVmTktO5JFq1ahULFy7kfe97X9xR8kpCzubmZsaMGUPfvn356Ec/GmxWz+lc2+VrkAeZ2ZLMlWY2HxjUlgeT9CNJiyX9JVr1uJltjS5/CPhF9Bh/BA6RdGCBTa4xs2eiy7+MtpHtcS+WNF/S/LbkzsZs7y8nhPq/3qRkTUpO55Lm7bff5swzz+S2227jgAMOiDtOTknJWV5ezqJFi1i7di3z5s1j6dKlcUfKynM613b5GuSqPLdVF7n9ZcDxrVfM7EvAyUCfaNU7abXZOiEDmtgzZ1XG7Zn1e2/E7E4zG29m44vMXdCAAQNYs2bNu9fXrl3LYYcd1lGb71BJyZqUnM4lSWNjI2eeeSbnnXcen/70p+OOk1NScqY76KCDmDhxIo8++mjcUfLynM6VLl+D/BdJ/5K5UtLngQVFbv+PQJWkL6atyzVP+GngvOgxJgJbzGw7sIqoyZZ0PDA47T5HSDohunwO8H9F5mq39773vbzyyiu89tprNDQ0cO+99zJ58uSueviSJCVrUnI6lxRmxuc//3lGjBjBVVddFXecnJKSE2Dz5s289dZbAOzatYsnnniC4cOHxxsqC8/pXDvl+vYe0A94FngSuDVaniJ1VIr+xX4LEDgUuBd4DZgHzCV1POULgR+m1fUCHgCWAH8GjovWVwOPAYuAnwIrSE3xGAQsB34c3ec3QE0ReayjPPzwwzZkyBA76qij7KabbmrXtujkb913VNbMnLafjunUqVOtf//+VlFRYYcffrjNmDGjzdvq7DHtzKz7Y06z5GT905/+ZICNHj3aamtrrba21h5++OF9OqdZ52ZdvHixjRkzxkaPHm2jRo2yG264oc3bSmpO68CsHZnTLHtWX3xpy1LwRCGSJgGtB/tcZqn5wbGTNAh4yMxKOhBpUg5unpSc0bpEZE1KzmhdIrJ6zvZLStak5ITkZE1qzmhdYrI61xYVhQrMbC6pvb77hKqqqo2S+sWdI1NVVdXGzOtJyNm6LglZk5KzdV0SsnrO9ktK1qTkhORkTWrO1nVJyepcWxR1qmnnnHPOOef2F/m+pOecc84559x+xxtk55xzzjnn0niD3AEkXRx3hmIkJSckJ2tSckJysiYlJyQna1JyQnKyes6Ol6Ssbt/nDXLHSMqTOik5ITlZk5ITkpM1KTkhOVmTkhOSk9VzdrwkZXX7OG+QnXPOOeecS+MNsnPOOeecc2m8Qe4Yd8YdoEhJyQnJyZqUnJCcrEnJCcnJmpSckJysnrPjJSmr28f5cZCdc84555xL43uQnXPOOeecS+MNsnPOOeecc2m8QW4HST+TtEnS0riz5CNpoKS5klZIWibp8rgzZSOpStI8SYujnDfEnakQSeWSFkp6KO4suUhaJelFSYskzY87Tz6SDpJ0v6S66O/1hLgzZZI0LBrL1mW7pCvizpWLpCuj59NSSfdIqoo7UzaSLo8yLgttPLO91kvqJelxSa9EPw+OM2OUKVvOKdGYtkgaH2e+dDmy/lf03F8i6XeSDooxotvPeYPcPjOB0+IOUYQm4GozGwG8H/iSpJExZ8qmHjjJzGqBMcBpkt4fb6SCLgdWxB2iCJPMbIyZBfMGmcMPgEfNbDhQS4Bja2YvRWM5BhgH7AR+F2+q7CQdDlwGjDezY4FyYGq8qfYm6VjgX4AJpP7dz5A0JN5Ue5jJ3q/11wF/MLMhwB+i63Gbyd45lwKfBp7u8jT5zWTvrI8Dx5rZccDLwNe6OpRzrbxBbgczexrYGneOQszsDTN7Ibq8g1TTcXi8qfZmKW9HV7tFS7DfIpU0APg4MCPuLPsCSQcAJwJ3AZhZg5m9FWuowk4GXjWz1XEHyaMCqJZUAdQA62POk80I4M9mttPMmoCngE/FnOldOV7rPwnMii7PAv6xKzNlky2nma0ws5diipRTjqyPRf/+AH8GBnR5MOci3iDvZyQNAsYCz8ccJatoysIiYBPwuJkFmTNyG/BVoCXmHIUY8JikBYGfyvUoYDNwdzRtZYak98QdqoCpwD1xh8jFzNYBtwCvA28A28zssXhTZbUUOFHSIZJqgI8BA2POVEg/M3sDUjshgL4x59nXfA54JO4Qbv/lDfJ+RFIP4DfAFWa2Pe482ZhZc/TR9QBgQvTRa3AknQFsMrMFcWcpwgfN7HjgdFLTa06MO1AOFcDxwH+b2VjgHcL42DorSd2BycCcuLPkEs2L/SQwGDgMeI+kf4o31d7MbAXwHVIfsT8KLCY1NczthyR9ndS//+y4s7j9lzfI+wlJ3Ug1x7PN7Ldx5ykk+mj9ScKd4/1BYLKkVcC9wEmSfhlvpOzMbH30cxOpubIT4k2U01pgbdqnBveTaphDdTrwgpltjDtIHqcAr5nZZjNrBH4LfCDmTFmZ2V1mdryZnUjqo/dX4s5UwEZJhwJEPzfFnGefIOkC4AzgPPMTNbgYeYO8H5AkUvM6V5jZ9+LOk4ukPq3fWpZUTerNvS7WUDmY2dfMbICZDSL1MfsfzSy4PXOS3iOpZ+tl4FRSH2cHx8w2AGskDYtWnQwsjzFSIecQ8PSKyOvA+yXVRK8DJxPgFx8BJPWNfh5B6ktloY/tg8AF0eULgAdizLJPkHQacC0w2cx2xp3H7d8q4g6QZJLuASYCvSWtBa43s7viTZXVB4F/Bl6M5vcC/JuZ/U98kbI6FJglqZzUf95+bWbBHj4tIfoBv0v1RlQAvzKzR+ONlNdXgNnR9IW/Ap+NOU9W0TzZjwJfiDtLPmb2vKT7gRdIfWS9kHBP5/sbSYcAjcCXzOxvcQdqle21HrgZ+LWkz5P6j8iU+BKm5Mi5Fbgd6AM8LGmRmf1DfClTcmT9GlAJPB69Zv3ZzC6JLaTbr/mppp1zzjnnnEvjUyycc84555xL4w2yc84555xzabxBds4555xzLo03yM4555xzzqXxBtk555xzzrk03iA75/YZkpolLZK0TNJiSVdJyvs6J2mQpHO7INsMSSML1PxjoRrnnHOdzxtk59y+ZJeZjTGzUaSOU/wxUsdXzWcQ0OkNspldZGaFTnzyj4A3yM45FzNvkJ1z+6To1NoXA19WyiBJf5L0QrS0nnL5ZuDD0Z7nK/PUvSuqqZM0S9ISSfdHJw9B0smSFkp6UdLPJFVG65+UND66/Lakb0d7uf8sqV/0OJOB/4qyHC3pMknLo8e4tyvGzTnnnJ8oxDm3D5H0tpn1yFj3N2A4sANoMbPdkoYA95jZeEkTgWvM7IyoviZbXcY2BwGvAR8ys2ck/YzUabF/CLwCnGxmL0v6OfCCmd0m6cnoceZLMlKn0/29pO8C283sJkkzgYfM7P7ocdYDg82sXtJBZvZWhw+ac865vfgeZOfcvk7Rz27ATyW9CMwh91SGYuvWmNkz0eVfAh8ChgGvmdnL0fpZwIlZ7tsAtJ5GfQGpaR7ZLCF16u1/InWqaOecc13AG2Tn3D5L0lFAM7AJuBLYCNQC44HuOe5WbF3mx2/G35vxQhrt7x/fNQMVOeo+DvwIGAcskJSrzjnnXAfyBtk5t0+S1Af4MfDDqBk9EHjDzFqAfwbKo9IdQM+0u+aqy3SEpBOiy+cA/wfUAYMkHROt/2fgqRJiv5slOvrGQDObC3wVOAjokfuuzjnnOoo3yM65fUl162HegCeAx4AbotvuAC6Q9GdgKPBOtH4J0BR9Ye7KPHWZVkR1S4BewH+b2W7gs8CcaIpGC6kmvVj3Av8qaSEwBPhltJ2FwPd9DrJzznUN/5Kec86VKPqS3kNmdmzcWZxzznU834PsnHPOOedcGt+D7JxzzjnnXBrfg+ycc84551wab5Cdc84555xL4w2yc84555xzabxBds4555xzLo03yM4555xzzqX5/wCPVTy/NmpeAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 720x144 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_group_kfold()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As you can see, for each split, each group is either entirely in the training set or entirely in the test set\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The following is an example of using a synthetic dataset with a grouping given by the groups array. The dataset consists of 12 data points, and for each of the data points, groups specifies which group (think patient) the point belongs to\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:\n",
      "[0.75       0.6        0.66666667]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import GroupKFold\n",
    "# create synthetic dataset\n",
    "X, y = make_blobs(n_samples=12, random_state=0)\n",
    "# assume the first three samples belong to the same group,\n",
    "# then the next four, etc.\n",
    "groups = [0, 0, 0, 1, 1, 1, 1, 2, 2, 3, 3, 3]\n",
    "scores = cross_val_score(logreg, X, y, groups=groups, cv=GroupKFold(n_splits=3))\n",
    "print(\"Cross-validation scores:\\n{}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Grid Search\n",
    "\n",
    "- we know how to evaluate how well a model generalizes, we can take the next step and improve the model’s generalization performance by tuning its parameters. <br><br>\n",
    "- We discussed the parameter settings of many of the algorithms in scikit-learn  and it is important to understand what the parameters mean before trying to adjust them. <br><br>\n",
    "- Finding the values of the important parameters of a model (the ones that provide the best generalization performance) is a tricky task, but necessary for almost all models and datasets. <br><br>\n",
    "- Because it is such a common task, there are standard methods in scikit-learn to help you with it. The most commonly used method is grid search, which basically means trying all possible combinations of the parameters of interest\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple Grid Search"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.90\n",
      "Accuracy on test set: 0.94\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split( cancer.data, cancer.target, random_state=0) \n",
    "\n",
    "svc = SVC() \n",
    "svc.fit(X_train, y_train) \n",
    "print(\"Accuracy on training set: {:.2f}\".format(svc.score(X_train, y_train))) \n",
    "print(\"Accuracy on test set: {:.2f}\".format(svc.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy on training set: 0.98\n",
      "Accuracy on test set: 0.96\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC\n",
    "from sklearn.datasets import load_breast_cancer\n",
    "\n",
    "cancer = load_breast_cancer()\n",
    "X_train, X_test, y_train, y_test = train_test_split( cancer.data, cancer.target, random_state=0) \n",
    "\n",
    "svm_re = SVC(kernel='poly', C=1000, gamma=0.00001).fit(X_train, y_train) # c= 1000, 0.001, \n",
    "print(\"Accuracy on training set: {:.2f}\".format(svm_re.score(X_train, y_train))) \n",
    "print(\"Accuracy on test set: {:.2f}\".format(svm_re.score(X_test, y_test)))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/svm/_base.py:1206: ConvergenceWarning: Liblinear failed to converge, increase the number of iterations.\n",
      "  warnings.warn(\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature 1')"
      ]
     },
     "execution_count": 41,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import LinearSVC \n",
    "linear_svm = LinearSVC().fit(X, y) \n",
    "mglearn.plots.plot_2d_separator(linear_svm, X)\n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y) \n",
    "plt.xlabel(\"Feature 0\") \n",
    "plt.ylabel(\"Feature 1\")\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Text(0, 0.5, 'Feature 1')"
      ]
     },
     "execution_count": 42,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.svm import SVC \n",
    "X, y = mglearn.tools.make_handcrafted_dataset() \n",
    "svm = SVC(kernel='rbf', C=10, gamma=0.1).fit(X, y) \n",
    "mglearn.plots.plot_2d_separator(svm, X, eps=.5) \n",
    "mglearn.discrete_scatter(X[:, 0], X[:, 1], y) \n",
    "\n",
    "# plot support vectors \n",
    "sv = svm.support_vectors_ \n",
    "# class labels of support vectors are given by the sign of the dual coefficients \n",
    "sv_labels = svm.dual_coef_.ravel() > 0 \n",
    "mglearn.discrete_scatter(sv[:, 0], sv[:, 1], sv_labels, s=15, markeredgewidth=3) \n",
    "plt.xlabel(\"Feature 0\") \n",
    "plt.ylabel(\"Feature 1\")\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Grid search,  means trying all possible combinations of the parameters of interest."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Simple Grid Search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Consider the case of a kernel SVM with an RBF (radial basis function) kernel, as implemented in the SVC class. <br><br>\n",
    "- There are two important parameters: the kernel bandwidth, **gamma**, and the regularization parameter,**C** <br><br>\n",
    "- Say we want to try the values 0.001, 0.01, 0.1, 1, 10, and 100 for the parameter C, and the same for gamma.  <br><br>\n",
    "- Because we have six different settings for C and gamma that we want to try, we have 36 combinations of parameters in total <br><br>\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We can implement a simple grid search just as for loops over the two parameters, training and evaluating a classifier for each combination\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 112   size of test set: 38\n",
      "Best score: 0.97\n",
      "Best parameters: {'C': 100, 'gamma': 0.001}\n"
     ]
    }
   ],
   "source": [
    "# naive grid search implementation\n",
    "from sklearn.svm import SVC\n",
    "from sklearn import datasets\n",
    "\n",
    "iris = datasets.load_iris()\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "print(\"Size of training set: {}   size of test set: {}\".format(\n",
    "      X_train.shape[0], X_test.shape[0]))\n",
    "\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters, train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the test set\n",
    "        score = svm.score(X_test, y_test)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "print(\"Best score: {:.2f}\".format(best_score))\n",
    "print(\"Best parameters: {}\".format(best_parameters))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### The danger of overfitting the parameters and the validation set"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There might be problem with previous approach <br><br>\n",
    "- We tried many different parameters and selected the one with best accuracy on the test set, but this accuracy won’t necessarily carry over to new data. <br><br>\n",
    "- Because we used the test data to adjust the parameters, we can no longer use it to assess how good the model is. <br><br>\n",
    "- Therfore, we need an independent dataset to evaluate, one that was not used to create the model <br><br>\n",
    "- One way to resolve this problem is to split the data again, so we have three sets: the training set to build the model, the validation (or development) set to select the parameters of the model, and the test set to evaluate the performance of the selected parameters. \n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/train_validation_set.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The validation dataset is different from the test dataset that is also held back from the training of the model, but is instead used to give an unbiased estimate of the skill of the final tuned model when comparing or selecting between final models.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "![](./images/validation_train_example.png)\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Ideally, the model should be evaluated on samples that were not used to build or fine-tune the model, so that they provide an unbiased sense of model effectiveness. When a large amount of data is at hand, a set of samples can be set aside to evaluate the final model. The “training” data set is the general term for the samples used to create the model, while the “test” or “validation” data set is used to qualify performance...— Max Kuhn and Kjell Johnson, Page 67, Applied Predictive Modeling, 2013"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* Training set: A set of examples used for learning, that is to fit the parameters of the classifier. <br><br>\n",
    "\n",
    "* Validation set: A set of examples used to tune the parameters of a classifier, for example to choose the number of hidden units in a neural network and hyper-parameters for tuning <br><br>\n",
    "\n",
    "* Test set: A set of examples used only to assess the performance of a fully-specified classifier <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> After selecting the best parameters using the validation set, we can rebuild a model using the parameter settings we found, but now training on both the training data and the validation data. \n",
    "> This way, we can use as much data as possible to build our model. This leads to the following implementation\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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     },
     "execution_count": 92,
     "metadata": {},
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   ],
   "source": [
    "pd.DataFrame(iris.target)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Size of training set: 84   size of validation set: 28   size of test set: 38\n",
      "\n",
      "Best score on validation set: 0.96\n",
      "Best parameters:  {'C': 10, 'gamma': 0.001}\n",
      "Test set score with best parameters: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# split data into train+validation set and test set\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "\n",
    "\n",
    "# split train+validation set into training and validation sets\n",
    "X_train, X_valid, y_train, y_valid = train_test_split(\n",
    "    X_trainval, y_trainval, random_state=1)\n",
    "print(\"Size of training set: {}   size of validation set: {}   size of test set:\"\n",
    "      \" {}\\n\".format(X_train.shape[0], X_valid.shape[0], X_test.shape[0]))\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters, train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        svm.fit(X_train, y_train)\n",
    "        # evaluate the SVC on the validation set\n",
    "        score = svm.score(X_valid, y_valid)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "\n",
    "# rebuild a model on the combined training and validation set,\n",
    "# and evaluate it on the test set\n",
    "#passing dictionary items as function arguments in python\n",
    "svm = SVC(**best_parameters) # * interprets arguments as tuples and ** interprets the arguments as dictionary\n",
    "svm.fit(X_trainval, y_trainval)\n",
    "test_score = svm.score(X_test, y_test)\n",
    "print(\"Best score on validation set: {:.2f}\".format(best_score))\n",
    "print(\"Best parameters: \", best_parameters)\n",
    "print(\"Test set score with best parameters: {:.2f}\".format(test_score))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The best score on the validation set is 96%: slightly lower than before, probably because we used less data to train the model (X_train is smaller now because we split our dataset twice). However, the score on the test set—the score that actually tells us how well we generalize—is even lower, at 92%. So we can only claim to classify new data 92% correctly, not 97% correctly as we thought before!\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Validation Dataset Is Not Enough\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- There are other ways of calculating an unbiased, (or progressively more biased in the case of the validation dataset) estimate of model skill on unseen data. <br><br>\n",
    "\n",
    "- One popular example is to use k-fold cross-validation to tune model hyperparameters instead of a separate validation dataset. <br><br>\n",
    "\n",
    "- In their book, Kuhn and Johnson have a section titled “Data Splitting Recommendations” in which they layout the limitations of using a sole “test set” (or validation set <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    ">  There is a strong technical case to be made against a single, independent test set:"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "* A test set is a single evaluation of the model and has limited ability to characterize the uncertainty in the results. <br><br>\n",
    "* Proportionally large test sets divide the data in a way that increases bias in the performance estimates. <br><br>\n",
    "* With small sample sizes: <br><br>\n",
    "  * The model may need every possible data point to adequately determine model values. <br><br>\n",
    "  * The uncertainty of the test set can be considerably large to the point where different test sets may produce very different results. <br><br>\n",
    "  * Resampling methods can produce reasonable predictions of how well the model will perform on future samples. <br><br>"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Grid Search with Cross-Validation\n",
    "\n",
    "- While the method of splitting the data into a training, a validation, and a test set that we just saw is workable, and relatively commonly used, it is quite sensitive to how exactly the data is split. <br><br>\n",
    "- From the output of the previous code snippet we can see that GridSearchCV selects 'C': 10, 'gamma': 0.001 as the best parameters, while the output of the code in the previous section selects 'C': 100, 'gamma': 0.001 as the best parameters. <br><br>\n",
    "- For a better estimate of the generalization performance, instead of using a single split into a training and a validation set, we can use cross-validation to evaluate the performance of each parameter combination as follows: <br><br>\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "SVC(C=10, gamma=0.1)"
      ]
     },
     "execution_count": 47,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "# split data into train+validation set and test set\n",
    "X_trainval, X_test, y_trainval, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)\n",
    "\n",
    "best_score = 0\n",
    "\n",
    "from sklearn.model_selection import cross_val_score\n",
    "for gamma in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "    for C in [0.001, 0.01, 0.1, 1, 10, 100]:\n",
    "        # for each combination of parameters,\n",
    "        # train an SVC\n",
    "        svm = SVC(gamma=gamma, C=C)\n",
    "        # perform cross-validation\n",
    "        scores = cross_val_score(svm, X_trainval, y_trainval, cv=5)\n",
    "        # compute mean cross-validation accuracy\n",
    "        score = np.mean(scores)\n",
    "        # if we got a better score, store the score and parameters\n",
    "        if score > best_score:\n",
    "            best_score = score\n",
    "            best_parameters = {'C': C, 'gamma': gamma}\n",
    "# rebuild a model on the combined training and validation set\n",
    "svm = SVC(**best_parameters)\n",
    "svm.fit(X_trainval, y_trainval)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "To evaluate the accuracy of the SVM using a particular setting of C and gamma using five-fold cross-validation, we need to train 36 * 5 = 180 models. As you can imagine, the main downside of the use of cross-validation is the time it takes to train all these models.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [],
   "source": [
    "mglearn.plots.plot_cross_val_selection()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The overall process of splitting the data, running the grid search, and evaluating the final parameters is illustrated in\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 700x210 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_grid_search_overview()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Cross-validation is a way to evaluate a given algorithm on a specific dataset. However, it is often used in conjunction with parameter search methods like grid search. For this reason, many people use the term cross-validation colloquially to refer to grid search with cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Because grid search with cross-validation is such a commonly used method to adjust parameters, Scikit-learn provides the GridSearchCV class, which implements it in the form of an estimator. <br><br>\n",
    "- To use the GridSearchCV class, you first need to specify the parameters you want to search over using a dictionary.  <br><br>\n",
    "-  The keys of the dictionary are the names of parameters we want to adjust (as given when constructing the model—in this case, C and gamma), and the values are the parameter settings we want to try out\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Parameter grid:\n",
      "{'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "print(\"Parameter grid:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can now instantiate the GridSearchCV class with the model (SVC), the parameter grid to search (param_grid), and the cross-validation strategy we want to use (say, five-fold stratified cross-validation)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.model_selection import GridSearchCV\n",
    "from sklearn.svm import SVC\n",
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
    "                          return_train_score=True)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- GridSearchCV will use cross-validation in place of the split into a training and validation set that we used before. <br><br>\n",
    "- However, we still need to split the data into a training and a test set, to avoid overfitting the parameters\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    iris.data, iris.target, random_state=0)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The grid_search object that we created behaves just like a classifier; we can call the standard methods fit, predict, and score on it. <br><br>\n",
    "- However, when we call fit, it will run cross-validation for each combination of parameters we specified in param_grid\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "GridSearchCV(cv=5, estimator=SVC(),\n",
       "             param_grid={'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
       "                         'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
       "             return_train_score=True)"
      ]
     },
     "execution_count": 75,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "grid_search.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Fitting the GridSearchCV object not only searches for the best parameters, but also automatically fits a new model on the whole training dataset with the parameters that yielded the best cross-validation performance. <br><br>\n",
    "\n",
    "- The GridSearchCV class provides a very convenient interface to access the retrained model using the predict and score methods. To evaluate how well the best found parameters generalize, we can call score on the test set:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test set score: 0.9736842105263158\n"
     ]
    }
   ],
   "source": [
    "print(f\"Test set score: {grid_search.score(X_test, y_test)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Choosing the parameters using cross-validation, we actually found a model that achieves 97% accuracy on the test set. The important thing here is that we did not use the test set to choose the parameters\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The parameters that were found are scored in the best_params_ attribute, and the best cross-validation accuracy (the mean accuracy over the different splits for this parameter setting) is stored in best_score_:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 10, 'gamma': 0.1}\n",
      "Best cross-validation score: 0.97\n"
     ]
    }
   ],
   "source": [
    "print(\"Best parameters: {}\".format(grid_search.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Do not to confuse best_score_ with the generalization performance of the model as computed by the score method on the test set. Using the score method (or evaluating the output of the predict method) employs a model trained on the whole training set. The best_score_ attribute stores the mean cross-validation accuracy, with cross-validation performed on the training set.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Sometimes it is helpful to have access to the actual model that was found—for example, to look at coefficients or feature importances. You can access the model with the best parameters trained on the whole training set using the best_estimator_ attribute\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best estimator:\n",
      "SVC(C=10, gamma=0.1)\n"
     ]
    }
   ],
   "source": [
    "print(\"Best estimator:\\n{}\".format(grid_search.best_estimator_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Help on GridSearchCV in module sklearn.model_selection._search object:\n",
      "\n",
      "class GridSearchCV(BaseSearchCV)\n",
      " |  GridSearchCV(estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)\n",
      " |  \n",
      " |  Exhaustive search over specified parameter values for an estimator.\n",
      " |  \n",
      " |  Important members are fit, predict.\n",
      " |  \n",
      " |  GridSearchCV implements a \"fit\" and a \"score\" method.\n",
      " |  It also implements \"score_samples\", \"predict\", \"predict_proba\",\n",
      " |  \"decision_function\", \"transform\" and \"inverse_transform\" if they are\n",
      " |  implemented in the estimator used.\n",
      " |  \n",
      " |  The parameters of the estimator used to apply these methods are optimized\n",
      " |  by cross-validated grid-search over a parameter grid.\n",
      " |  \n",
      " |  Read more in the :ref:`User Guide <grid_search>`.\n",
      " |  \n",
      " |  Parameters\n",
      " |  ----------\n",
      " |  estimator : estimator object\n",
      " |      This is assumed to implement the scikit-learn estimator interface.\n",
      " |      Either estimator needs to provide a ``score`` function,\n",
      " |      or ``scoring`` must be passed.\n",
      " |  \n",
      " |  param_grid : dict or list of dictionaries\n",
      " |      Dictionary with parameters names (`str`) as keys and lists of\n",
      " |      parameter settings to try as values, or a list of such\n",
      " |      dictionaries, in which case the grids spanned by each dictionary\n",
      " |      in the list are explored. This enables searching over any sequence\n",
      " |      of parameter settings.\n",
      " |  \n",
      " |  scoring : str, callable, list, tuple or dict, default=None\n",
      " |      Strategy to evaluate the performance of the cross-validated model on\n",
      " |      the test set.\n",
      " |  \n",
      " |      If `scoring` represents a single score, one can use:\n",
      " |  \n",
      " |      - a single string (see :ref:`scoring_parameter`);\n",
      " |      - a callable (see :ref:`scoring`) that returns a single value.\n",
      " |  \n",
      " |      If `scoring` represents multiple scores, one can use:\n",
      " |  \n",
      " |      - a list or tuple of unique strings;\n",
      " |      - a callable returning a dictionary where the keys are the metric\n",
      " |        names and the values are the metric scores;\n",
      " |      - a dictionary with metric names as keys and callables a values.\n",
      " |  \n",
      " |      See :ref:`multimetric_grid_search` for an example.\n",
      " |  \n",
      " |  n_jobs : int, default=None\n",
      " |      Number of jobs to run in parallel.\n",
      " |      ``None`` means 1 unless in a :obj:`joblib.parallel_backend` context.\n",
      " |      ``-1`` means using all processors. See :term:`Glossary <n_jobs>`\n",
      " |      for more details.\n",
      " |  \n",
      " |      .. versionchanged:: v0.20\n",
      " |         `n_jobs` default changed from 1 to None\n",
      " |  \n",
      " |  refit : bool, str, or callable, default=True\n",
      " |      Refit an estimator using the best found parameters on the whole\n",
      " |      dataset.\n",
      " |  \n",
      " |      For multiple metric evaluation, this needs to be a `str` denoting the\n",
      " |      scorer that would be used to find the best parameters for refitting\n",
      " |      the estimator at the end.\n",
      " |  \n",
      " |      Where there are considerations other than maximum score in\n",
      " |      choosing a best estimator, ``refit`` can be set to a function which\n",
      " |      returns the selected ``best_index_`` given ``cv_results_``. In that\n",
      " |      case, the ``best_estimator_`` and ``best_params_`` will be set\n",
      " |      according to the returned ``best_index_`` while the ``best_score_``\n",
      " |      attribute will not be available.\n",
      " |  \n",
      " |      The refitted estimator is made available at the ``best_estimator_``\n",
      " |      attribute and permits using ``predict`` directly on this\n",
      " |      ``GridSearchCV`` instance.\n",
      " |  \n",
      " |      Also for multiple metric evaluation, the attributes ``best_index_``,\n",
      " |      ``best_score_`` and ``best_params_`` will only be available if\n",
      " |      ``refit`` is set and all of them will be determined w.r.t this specific\n",
      " |      scorer.\n",
      " |  \n",
      " |      See ``scoring`` parameter to know more about multiple metric\n",
      " |      evaluation.\n",
      " |  \n",
      " |      .. versionchanged:: 0.20\n",
      " |          Support for callable added.\n",
      " |  \n",
      " |  cv : int, cross-validation generator or an iterable, default=None\n",
      " |      Determines the cross-validation splitting strategy.\n",
      " |      Possible inputs for cv are:\n",
      " |  \n",
      " |      - None, to use the default 5-fold cross validation,\n",
      " |      - integer, to specify the number of folds in a `(Stratified)KFold`,\n",
      " |      - :term:`CV splitter`,\n",
      " |      - An iterable yielding (train, test) splits as arrays of indices.\n",
      " |  \n",
      " |      For integer/None inputs, if the estimator is a classifier and ``y`` is\n",
      " |      either binary or multiclass, :class:`StratifiedKFold` is used. In all\n",
      " |      other cases, :class:`KFold` is used. These splitters are instantiated\n",
      " |      with `shuffle=False` so the splits will be the same across calls.\n",
      " |  \n",
      " |      Refer :ref:`User Guide <cross_validation>` for the various\n",
      " |      cross-validation strategies that can be used here.\n",
      " |  \n",
      " |      .. versionchanged:: 0.22\n",
      " |          ``cv`` default value if None changed from 3-fold to 5-fold.\n",
      " |  \n",
      " |  verbose : int\n",
      " |      Controls the verbosity: the higher, the more messages.\n",
      " |  \n",
      " |      - >1 : the computation time for each fold and parameter candidate is\n",
      " |        displayed;\n",
      " |      - >2 : the score is also displayed;\n",
      " |      - >3 : the fold and candidate parameter indexes are also displayed\n",
      " |        together with the starting time of the computation.\n",
      " |  \n",
      " |  pre_dispatch : int, or str, default='2*n_jobs'\n",
      " |      Controls the number of jobs that get dispatched during parallel\n",
      " |      execution. Reducing this number can be useful to avoid an\n",
      " |      explosion of memory consumption when more jobs get dispatched\n",
      " |      than CPUs can process. This parameter can be:\n",
      " |  \n",
      " |          - None, in which case all the jobs are immediately\n",
      " |            created and spawned. Use this for lightweight and\n",
      " |            fast-running jobs, to avoid delays due to on-demand\n",
      " |            spawning of the jobs\n",
      " |  \n",
      " |          - An int, giving the exact number of total jobs that are\n",
      " |            spawned\n",
      " |  \n",
      " |          - A str, giving an expression as a function of n_jobs,\n",
      " |            as in '2*n_jobs'\n",
      " |  \n",
      " |  error_score : 'raise' or numeric, default=np.nan\n",
      " |      Value to assign to the score if an error occurs in estimator fitting.\n",
      " |      If set to 'raise', the error is raised. If a numeric value is given,\n",
      " |      FitFailedWarning is raised. This parameter does not affect the refit\n",
      " |      step, which will always raise the error.\n",
      " |  \n",
      " |  return_train_score : bool, default=False\n",
      " |      If ``False``, the ``cv_results_`` attribute will not include training\n",
      " |      scores.\n",
      " |      Computing training scores is used to get insights on how different\n",
      " |      parameter settings impact the overfitting/underfitting trade-off.\n",
      " |      However computing the scores on the training set can be computationally\n",
      " |      expensive and is not strictly required to select the parameters that\n",
      " |      yield the best generalization performance.\n",
      " |  \n",
      " |      .. versionadded:: 0.19\n",
      " |  \n",
      " |      .. versionchanged:: 0.21\n",
      " |          Default value was changed from ``True`` to ``False``\n",
      " |  \n",
      " |  Attributes\n",
      " |  ----------\n",
      " |  cv_results_ : dict of numpy (masked) ndarrays\n",
      " |      A dict with keys as column headers and values as columns, that can be\n",
      " |      imported into a pandas ``DataFrame``.\n",
      " |  \n",
      " |      For instance the below given table\n",
      " |  \n",
      " |      +------------+-----------+------------+-----------------+---+---------+\n",
      " |      |param_kernel|param_gamma|param_degree|split0_test_score|...|rank_t...|\n",
      " |      +============+===========+============+=================+===+=========+\n",
      " |      |  'poly'    |     --    |      2     |       0.80      |...|    2    |\n",
      " |      +------------+-----------+------------+-----------------+---+---------+\n",
      " |      |  'poly'    |     --    |      3     |       0.70      |...|    4    |\n",
      " |      +------------+-----------+------------+-----------------+---+---------+\n",
      " |      |  'rbf'     |     0.1   |     --     |       0.80      |...|    3    |\n",
      " |      +------------+-----------+------------+-----------------+---+---------+\n",
      " |      |  'rbf'     |     0.2   |     --     |       0.93      |...|    1    |\n",
      " |      +------------+-----------+------------+-----------------+---+---------+\n",
      " |  \n",
      " |      will be represented by a ``cv_results_`` dict of::\n",
      " |  \n",
      " |          {\n",
      " |          'param_kernel': masked_array(data = ['poly', 'poly', 'rbf', 'rbf'],\n",
      " |                                       mask = [False False False False]...)\n",
      " |          'param_gamma': masked_array(data = [-- -- 0.1 0.2],\n",
      " |                                      mask = [ True  True False False]...),\n",
      " |          'param_degree': masked_array(data = [2.0 3.0 -- --],\n",
      " |                                       mask = [False False  True  True]...),\n",
      " |          'split0_test_score'  : [0.80, 0.70, 0.80, 0.93],\n",
      " |          'split1_test_score'  : [0.82, 0.50, 0.70, 0.78],\n",
      " |          'mean_test_score'    : [0.81, 0.60, 0.75, 0.85],\n",
      " |          'std_test_score'     : [0.01, 0.10, 0.05, 0.08],\n",
      " |          'rank_test_score'    : [2, 4, 3, 1],\n",
      " |          'split0_train_score' : [0.80, 0.92, 0.70, 0.93],\n",
      " |          'split1_train_score' : [0.82, 0.55, 0.70, 0.87],\n",
      " |          'mean_train_score'   : [0.81, 0.74, 0.70, 0.90],\n",
      " |          'std_train_score'    : [0.01, 0.19, 0.00, 0.03],\n",
      " |          'mean_fit_time'      : [0.73, 0.63, 0.43, 0.49],\n",
      " |          'std_fit_time'       : [0.01, 0.02, 0.01, 0.01],\n",
      " |          'mean_score_time'    : [0.01, 0.06, 0.04, 0.04],\n",
      " |          'std_score_time'     : [0.00, 0.00, 0.00, 0.01],\n",
      " |          'params'             : [{'kernel': 'poly', 'degree': 2}, ...],\n",
      " |          }\n",
      " |  \n",
      " |      NOTE\n",
      " |  \n",
      " |      The key ``'params'`` is used to store a list of parameter\n",
      " |      settings dicts for all the parameter candidates.\n",
      " |  \n",
      " |      The ``mean_fit_time``, ``std_fit_time``, ``mean_score_time`` and\n",
      " |      ``std_score_time`` are all in seconds.\n",
      " |  \n",
      " |      For multi-metric evaluation, the scores for all the scorers are\n",
      " |      available in the ``cv_results_`` dict at the keys ending with that\n",
      " |      scorer's name (``'_<scorer_name>'``) instead of ``'_score'`` shown\n",
      " |      above. ('split0_test_precision', 'mean_train_precision' etc.)\n",
      " |  \n",
      " |  best_estimator_ : estimator\n",
      " |      Estimator that was chosen by the search, i.e. estimator\n",
      " |      which gave highest score (or smallest loss if specified)\n",
      " |      on the left out data. Not available if ``refit=False``.\n",
      " |  \n",
      " |      See ``refit`` parameter for more information on allowed values.\n",
      " |  \n",
      " |  best_score_ : float\n",
      " |      Mean cross-validated score of the best_estimator\n",
      " |  \n",
      " |      For multi-metric evaluation, this is present only if ``refit`` is\n",
      " |      specified.\n",
      " |  \n",
      " |      This attribute is not available if ``refit`` is a function.\n",
      " |  \n",
      " |  best_params_ : dict\n",
      " |      Parameter setting that gave the best results on the hold out data.\n",
      " |  \n",
      " |      For multi-metric evaluation, this is present only if ``refit`` is\n",
      " |      specified.\n",
      " |  \n",
      " |  best_index_ : int\n",
      " |      The index (of the ``cv_results_`` arrays) which corresponds to the best\n",
      " |      candidate parameter setting.\n",
      " |  \n",
      " |      The dict at ``search.cv_results_['params'][search.best_index_]`` gives\n",
      " |      the parameter setting for the best model, that gives the highest\n",
      " |      mean score (``search.best_score_``).\n",
      " |  \n",
      " |      For multi-metric evaluation, this is present only if ``refit`` is\n",
      " |      specified.\n",
      " |  \n",
      " |  scorer_ : function or a dict\n",
      " |      Scorer function used on the held out data to choose the best\n",
      " |      parameters for the model.\n",
      " |  \n",
      " |      For multi-metric evaluation, this attribute holds the validated\n",
      " |      ``scoring`` dict which maps the scorer key to the scorer callable.\n",
      " |  \n",
      " |  n_splits_ : int\n",
      " |      The number of cross-validation splits (folds/iterations).\n",
      " |  \n",
      " |  refit_time_ : float\n",
      " |      Seconds used for refitting the best model on the whole dataset.\n",
      " |  \n",
      " |      This is present only if ``refit`` is not False.\n",
      " |  \n",
      " |      .. versionadded:: 0.20\n",
      " |  \n",
      " |  multimetric_ : bool\n",
      " |      Whether or not the scorers compute several metrics.\n",
      " |  \n",
      " |  classes_ : ndarray of shape (n_classes,)\n",
      " |      The classes labels. This is present only if ``refit`` is specified and\n",
      " |      the underlying estimator is a classifier.\n",
      " |  \n",
      " |  n_features_in_ : int\n",
      " |      Number of features seen during :term:`fit`. Only defined if\n",
      " |      `best_estimator_` is defined (see the documentation for the `refit`\n",
      " |      parameter for more details) and that `best_estimator_` exposes\n",
      " |      `n_features_in_` when fit.\n",
      " |  \n",
      " |      .. versionadded:: 0.24\n",
      " |  \n",
      " |  feature_names_in_ : ndarray of shape (`n_features_in_`,)\n",
      " |      Names of features seen during :term:`fit`. Only defined if\n",
      " |      `best_estimator_` is defined (see the documentation for the `refit`\n",
      " |      parameter for more details) and that `best_estimator_` exposes\n",
      " |      `feature_names_in_` when fit.\n",
      " |  \n",
      " |      .. versionadded:: 1.0\n",
      " |  \n",
      " |  Notes\n",
      " |  -----\n",
      " |  The parameters selected are those that maximize the score of the left out\n",
      " |  data, unless an explicit score is passed in which case it is used instead.\n",
      " |  \n",
      " |  If `n_jobs` was set to a value higher than one, the data is copied for each\n",
      " |  point in the grid (and not `n_jobs` times). This is done for efficiency\n",
      " |  reasons if individual jobs take very little time, but may raise errors if\n",
      " |  the dataset is large and not enough memory is available.  A workaround in\n",
      " |  this case is to set `pre_dispatch`. Then, the memory is copied only\n",
      " |  `pre_dispatch` many times. A reasonable value for `pre_dispatch` is `2 *\n",
      " |  n_jobs`.\n",
      " |  \n",
      " |  See Also\n",
      " |  ---------\n",
      " |  ParameterGrid : Generates all the combinations of a hyperparameter grid.\n",
      " |  train_test_split : Utility function to split the data into a development\n",
      " |      set usable for fitting a GridSearchCV instance and an evaluation set\n",
      " |      for its final evaluation.\n",
      " |  sklearn.metrics.make_scorer : Make a scorer from a performance metric or\n",
      " |      loss function.\n",
      " |  \n",
      " |  Examples\n",
      " |  --------\n",
      " |  >>> from sklearn import svm, datasets\n",
      " |  >>> from sklearn.model_selection import GridSearchCV\n",
      " |  >>> iris = datasets.load_iris()\n",
      " |  >>> parameters = {'kernel':('linear', 'rbf'), 'C':[1, 10]}\n",
      " |  >>> svc = svm.SVC()\n",
      " |  >>> clf = GridSearchCV(svc, parameters)\n",
      " |  >>> clf.fit(iris.data, iris.target)\n",
      " |  GridSearchCV(estimator=SVC(),\n",
      " |               param_grid={'C': [1, 10], 'kernel': ('linear', 'rbf')})\n",
      " |  >>> sorted(clf.cv_results_.keys())\n",
      " |  ['mean_fit_time', 'mean_score_time', 'mean_test_score',...\n",
      " |   'param_C', 'param_kernel', 'params',...\n",
      " |   'rank_test_score', 'split0_test_score',...\n",
      " |   'split2_test_score', ...\n",
      " |   'std_fit_time', 'std_score_time', 'std_test_score']\n",
      " |  \n",
      " |  Method resolution order:\n",
      " |      GridSearchCV\n",
      " |      BaseSearchCV\n",
      " |      sklearn.base.MetaEstimatorMixin\n",
      " |      sklearn.base.BaseEstimator\n",
      " |      builtins.object\n",
      " |  \n",
      " |  Methods defined here:\n",
      " |  \n",
      " |  __init__(self, estimator, param_grid, *, scoring=None, n_jobs=None, refit=True, cv=None, verbose=0, pre_dispatch='2*n_jobs', error_score=nan, return_train_score=False)\n",
      " |      Initialize self.  See help(type(self)) for accurate signature.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data and other attributes defined here:\n",
      " |  \n",
      " |  __abstractmethods__ = frozenset()\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from BaseSearchCV:\n",
      " |  \n",
      " |  decision_function(self, X)\n",
      " |      Call decision_function on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if ``refit=True`` and the underlying estimator supports\n",
      " |      ``decision_function``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_score : ndarray of shape (n_samples,) or (n_samples, n_classes)                 or (n_samples, n_classes * (n_classes-1) / 2)\n",
      " |          Result of the decision function for `X` based on the estimator with\n",
      " |          the best found parameters.\n",
      " |  \n",
      " |  fit(self, X, y=None, *, groups=None, **fit_params)\n",
      " |      Run fit with all sets of parameters.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      \n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Training vector, where `n_samples` is the number of samples and\n",
      " |          `n_features` is the number of features.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples, n_output)             or (n_samples,), default=None\n",
      " |          Target relative to X for classification or regression;\n",
      " |          None for unsupervised learning.\n",
      " |      \n",
      " |      groups : array-like of shape (n_samples,), default=None\n",
      " |          Group labels for the samples used while splitting the dataset into\n",
      " |          train/test set. Only used in conjunction with a \"Group\" :term:`cv`\n",
      " |          instance (e.g., :class:`~sklearn.model_selection.GroupKFold`).\n",
      " |      \n",
      " |      **fit_params : dict of str -> object\n",
      " |          Parameters passed to the ``fit`` method of the estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : object\n",
      " |          Instance of fitted estimator.\n",
      " |  \n",
      " |  inverse_transform(self, Xt)\n",
      " |      Call inverse_transform on the estimator with the best found params.\n",
      " |      \n",
      " |      Only available if the underlying estimator implements\n",
      " |      ``inverse_transform`` and ``refit=True``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      Xt : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      X : {ndarray, sparse matrix} of shape (n_samples, n_features)\n",
      " |          Result of the `inverse_transform` function for `Xt` based on the\n",
      " |          estimator with the best found parameters.\n",
      " |  \n",
      " |  predict(self, X)\n",
      " |      Call predict on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if ``refit=True`` and the underlying estimator supports\n",
      " |      ``predict``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,)\n",
      " |          The predicted labels or values for `X` based on the estimator with\n",
      " |          the best found parameters.\n",
      " |  \n",
      " |  predict_log_proba(self, X)\n",
      " |      Call predict_log_proba on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if ``refit=True`` and the underlying estimator supports\n",
      " |      ``predict_log_proba``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)\n",
      " |          Predicted class log-probabilities for `X` based on the estimator\n",
      " |          with the best found parameters. The order of the classes\n",
      " |          corresponds to that in the fitted attribute :term:`classes_`.\n",
      " |  \n",
      " |  predict_proba(self, X)\n",
      " |      Call predict_proba on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if ``refit=True`` and the underlying estimator supports\n",
      " |      ``predict_proba``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_pred : ndarray of shape (n_samples,) or (n_samples, n_classes)\n",
      " |          Predicted class probabilities for `X` based on the estimator with\n",
      " |          the best found parameters. The order of the classes corresponds\n",
      " |          to that in the fitted attribute :term:`classes_`.\n",
      " |  \n",
      " |  score(self, X, y=None)\n",
      " |      Return the score on the given data, if the estimator has been refit.\n",
      " |      \n",
      " |      This uses the score defined by ``scoring`` where provided, and the\n",
      " |      ``best_estimator_.score`` method otherwise.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : array-like of shape (n_samples, n_features)\n",
      " |          Input data, where `n_samples` is the number of samples and\n",
      " |          `n_features` is the number of features.\n",
      " |      \n",
      " |      y : array-like of shape (n_samples, n_output)             or (n_samples,), default=None\n",
      " |          Target relative to X for classification or regression;\n",
      " |          None for unsupervised learning.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      score : float\n",
      " |          The score defined by ``scoring`` if provided, and the\n",
      " |          ``best_estimator_.score`` method otherwise.\n",
      " |  \n",
      " |  score_samples(self, X)\n",
      " |      Call score_samples on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if ``refit=True`` and the underlying estimator supports\n",
      " |      ``score_samples``.\n",
      " |      \n",
      " |      .. versionadded:: 0.24\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : iterable\n",
      " |          Data to predict on. Must fulfill input requirements\n",
      " |          of the underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      y_score : ndarray of shape (n_samples,)\n",
      " |          The ``best_estimator_.score_samples`` method.\n",
      " |  \n",
      " |  transform(self, X)\n",
      " |      Call transform on the estimator with the best found parameters.\n",
      " |      \n",
      " |      Only available if the underlying estimator supports ``transform`` and\n",
      " |      ``refit=True``.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      X : indexable, length n_samples\n",
      " |          Must fulfill the input assumptions of the\n",
      " |          underlying estimator.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      Xt : {ndarray, sparse matrix} of shape (n_samples, n_features)\n",
      " |          `X` transformed in the new space based on the estimator with\n",
      " |          the best found parameters.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Readonly properties inherited from BaseSearchCV:\n",
      " |  \n",
      " |  classes_\n",
      " |      Class labels.\n",
      " |      \n",
      " |      Only available when `refit=True` and the estimator is a classifier.\n",
      " |  \n",
      " |  n_features_in_\n",
      " |      Number of features seen during :term:`fit`.\n",
      " |      \n",
      " |      Only available when `refit=True`.\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Data descriptors inherited from sklearn.base.MetaEstimatorMixin:\n",
      " |  \n",
      " |  __dict__\n",
      " |      dictionary for instance variables (if defined)\n",
      " |  \n",
      " |  __weakref__\n",
      " |      list of weak references to the object (if defined)\n",
      " |  \n",
      " |  ----------------------------------------------------------------------\n",
      " |  Methods inherited from sklearn.base.BaseEstimator:\n",
      " |  \n",
      " |  __getstate__(self)\n",
      " |  \n",
      " |  __repr__(self, N_CHAR_MAX=700)\n",
      " |      Return repr(self).\n",
      " |  \n",
      " |  __setstate__(self, state)\n",
      " |  \n",
      " |  get_params(self, deep=True)\n",
      " |      Get parameters for this estimator.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      deep : bool, default=True\n",
      " |          If True, will return the parameters for this estimator and\n",
      " |          contained subobjects that are estimators.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      params : dict\n",
      " |          Parameter names mapped to their values.\n",
      " |  \n",
      " |  set_params(self, **params)\n",
      " |      Set the parameters of this estimator.\n",
      " |      \n",
      " |      The method works on simple estimators as well as on nested objects\n",
      " |      (such as :class:`~sklearn.pipeline.Pipeline`). The latter have\n",
      " |      parameters of the form ``<component>__<parameter>`` so that it's\n",
      " |      possible to update each component of a nested object.\n",
      " |      \n",
      " |      Parameters\n",
      " |      ----------\n",
      " |      **params : dict\n",
      " |          Estimator parameters.\n",
      " |      \n",
      " |      Returns\n",
      " |      -------\n",
      " |      self : estimator instance\n",
      " |          Estimator instance.\n",
      "\n"
     ]
    }
   ],
   "source": [
    "grid_search."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Because grid_search itself has predict and score methods, using best_estimator_ is not needed to make predictions or evaluate the model.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Analyzing the result of cross-validation"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- It is often helpful to visualize the results of cross-validation, to understand how the model generalization depends on the parameters we are searching. <br><br>\n",
    "-  The results of a grid search can be found in the cv_results_ attribute, which is a dictionary storing all aspects of the search.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'grid_search' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/l_/499_8k_5387b9tz3wp5g8lg00000gn/T/ipykernel_98063/4216498628.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mpandas\u001b[0m \u001b[0;32mas\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      2\u001b[0m \u001b[0;31m# convert to Dataframe\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 3\u001b[0;31m \u001b[0mresults\u001b[0m \u001b[0;34m=\u001b[0m \u001b[0mpd\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mDataFrame\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mgrid_search\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mcv_results_\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0m\u001b[1;32m      4\u001b[0m \u001b[0;31m# show the first 5 rows\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[1;32m      5\u001b[0m \u001b[0mdisplay\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mresults\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'grid_search' is not defined"
     ]
    }
   ],
   "source": [
    "import pandas as pd\n",
    "# convert to Dataframe\n",
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# show the first 5 rows\n",
    "display(results)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each row in results corresponds to one particular parameter setting. For each setting, the results of all cross-validation splits are recorded, as well as the mean and standard deviation over all splits. <br><br>\n",
    "- As we were searching a two-dimensional grid of parameters (C and gamma), this is best visualized as a heat map <br><br>\n",
    "- First we extract the mean validation scores, then we reshape the scores so that the axes correspond to C and gamma\n",
    "\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.collections.PolyCollection at 0x1482110a0>"
      ]
     },
     "execution_count": 83,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
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\n",
      "image/png": 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gYpuGM/rs3oyaMp+rnpmHxWJhyGmpdc9S557mewsYUm3av4A1WusUYI3vfsC2bnHRNslKm7Y27HbFxZc1YvVK/40kPc3NmQO8nzwdkm1k7TMoLPBW9twcg3WfVTB8VP1cl2Xz9xV0SLLRrm0Idrvi6mHhfLKizK/N9jQX5wz0jk5SU+xk7HOTV2BQctDDlxsruGG0941styuimloDyvPt9+V0SAqhvS/PiGERLFlxyK/Nz7ucnDvQ+/o7pdjZs89NXoH/G3/NF2V0SAqhbevAit6WLS6Skqy09a2vS4eFsXJluV+btDSDAQO9I7vkZBuZmQYFBXX7JD6RnT+Uk9DWTnwbOyF2xdmXRPLVqlK/NnvTKuh9lnf5tOkQSl6WiwO+5dOjb2Miour3rditbRz7CorJ2u/AbXhY/t1OBnXvUOv+VouF0BAbVouiUYiNgpLSE3c6hqAtMlrr9UBRtcnDgLm+23OBy+vjufJyDeISqt6IcfEW8nL9N8hOnW2sXObdkH/43kl2lkFujgeAJx8p4b4HIrHU09LMzjVolVA1KmoVbyM7xz9P9y52Fi/zFp5N31ewN9NNVo6b3RkuYppbuGVCIf0HZ/PPewo5VOYJKE9WrpvWrfzzZOX6F5CeXUL5cKl3Q/z2+3IyMl1kZvu3WbS4lJGXRwSUBSA3x0NCfNX6io+zkpfj/xo7d7Gx3Le+tnzvJCvTIMfXRinFdaOLuGhoIfPf8S/edVGY66JFfNXyaRFvY3+e/0i4fecwvlzhHU3t+OEweVkuCqotw/oUG9WE3OKq0Vt+cSktmx49guzRLp737r+Ol8ddToc47y5evuMQcz/7HysevZnVT4zlYHkFX+/YW+csQVtkjqGl1joHwPc39lgNlVJjlVKblVKbi4qO/ybTuqb+/vdvua0JJQ4Pl11YwLy3yujcNQSrDdauLqd5cwvdegT26XyyeSaOb8qBYg/9zs9i5uwSenazY7Mq3AZs2ebk5usj2bgqgfDGiikzHKbnuf/2ZhxwGJx2fgYz3iymd7dQbLaqRk6n5uMVpVx9aWC7SgA1xDkqz623heNwaIZcUMicOWV07WbD5qsD//0wmqXLY3h7XjPenlvGNxudRz9ggKrnGTEumlKHh3EX72bx3AMkdwnDauIRUVXDNF1tRW7PzGfI5De55tl3WLB+C9NuvhSAiEahnNO9PRc9OpvBk16nkT2Ei/t0qnOWU/bAr9Z6FjALoHsPe03bZaW4eCu52VUjhdwcD7Et/XcxmkRYeOb5qN8em3PPKqB1ayufLjnMmlXlfL62gooKTelBDxPvOMCUF+t+uKhVvJWsI0YBWTlu4uP880RGWJj1Qkxlns79MklqY6PssIdW8Vb6nubdtbvikvCAi0xivI19Wf55Elr6bzqREVZmvxBXmadD3z20a1PVZtlnh+jdPYyWLQLf5OLjLX4ju5xcg9g4/8/LiAgLU59vWplnwJne9QUQ51uWMTFWLhwSypYtLvr1t9c5T0xcCAU5VcunIMdNdKz/h054hJWJ/4mvzHP9X38hLrH+PpiqyysuJS6qatQYG9WE/BL/XdxD5VXF9cuf9/DAcAtR4WGckdKarP0lHCg9DMCaH9Lp2S6BTzfvqFOWP9pIJk8pFQ/g+5tfHw/avWcIe/YY7NvrxunUfLrkMOcN9r8Ob4nDg9PprVXvLThMn352mkRYmPivSL7Y1JK1X8cy7eUo+g8IDajAAJzeK5T03W727HXhdGo+WHyIiy/wP95T7DAq88x5t5SB/cOIjLAQF2sjMcHGrnTvcH3tF4fpHOCB3zN6hZG+28luX55Fiw9y6YXhx8zzxvwS/tK/EZERVYVx4UcHGXlF4KMYgJ49Q9i922Cvb319vLicwdXWl+OI9bXg3cP07WcnIsJCWZmH0lLvyLaszMMX652kpgZW+FJ7hJG1x0nOPicup+bzT0o483z/11paYuDy5Vm2yEH3vo0JjwjsWNnx/LQ3lzYtmtEqOhKb1Xvg9vNtv/q1aR5RtU11a9MSi1IUHyon98BBeiTFExbiXS79OrZhd171Ixe190cbySwB/gY84/u7uD4e1GZTPPx4JDddV4RhwNUjGpGSGsKCed7KP2pMOL+ku7nvrmIsVkhOsfHUf6Lq46mPmef5J6O5bHQehgHXj2xCl1Q7r7/tPaP1j+sj2Znm4uY7C7FaoFNHO69OrTplOvWJaG4cX4DLpUlqY+O1aYGdQbHZFC8+FcvQUVkYBtw4MpKuqaHMnFsMwLi/RbE9zckNd+RhtUDnjnbeeL5lZf+yMg+r15cx87lj7t2edJ7HH49kzLUHMDwwYkQjUlNDmDfPe3xlzJjGpKe7mXCnA6sVUlJsPDfFO6opKPAw9mZvbrcBl18exqBzAruwu9WmGP9ISx742z48HrhweFOSOobyyfwDAFxybTP2pjt57p5sLFZF22Q7dz8bX9n/qTuy2PpNGY4DBqPPSmfMnTEMHREVUCbDo3n6g8949dYrsVgUH238iV9y9zN8QA8A3t+wlcG9UrhmYE/cHg8VLjf3z10KwLaMXFZtSWPhfddiGB52ZBXwwVfb6pxFVd9PCxZKqQXAICAGyAMmAx8B7wFtgL3AcK31CUts9x52/X/1cKqyvgTbrxWEKvOG7XWRFWS/VrDDGdXQEY4ycdrYho7gZ+tLd/9Pa92npnlBO5LRWo86xqzzftcgQoiA/NGOyQgh/mCkyAghTCVFRghhKikyQghTSZERQphKiowQwlRSZIQQppIiI4QwlRQZIYSppMgIIUwlRUYIYSopMkIIUwXtFyTr066SWM5ffmdDx6i0esi0ho7gJ9G8y5qcEmKtdb++rVmifnGduFGQkJGMEMJUUmSEEKaSIiOEMJUUGSGEqaTICCFMJUVGCGEqKTJCCFNJkRFCmEqKjBDCVFJkhBCmkiIjhDCVFBkhhKmkyAghTPWn+BZ2bZydmMTk/udhVYqFO7fy6tZv/ebf0v0MhiV3AcCmFMlRzek9/2UcFeUn7FsX69eW8+QjJRgGDB/VmFtua+I331Hs4d8Ti9mXYWAPVTw9pSkdO1X9prVhaK68uJCWcVZmvRUdcJ6Va8u496EiDA/cMKoJE2+P8pt/oNhg3N372Z3hIjRUMfP5GLp2sgNQ7DC4deJ+ft7hRCnFzOeb069PWEB51q2t4JHJ3uUzclQjbhvvv3yKiz3ce4+DjAyD0FDFlKmRpPqWz1n98wkPt2C1gtUGn9bD76RvWFfOc48W4zE0V4wM5++3RvrNL3F4mHxvEZkZBvZQePQ/0SSnhlBRrvn7Nfm4nOB2a86/qBG33t004DwAfU9vx+1jz8NiUXy6civvvv+N3/xe3Vvz5ENXkpNXDMAXX6Uxd8FXtep7Mv6QRUYpNRu4BMjXWncL9PEsSvH4WYO5dtl75B46yJJhY1i99xfSivdXtnlt2yZe27YJgPPadODmbqfjqCivVd+TZRiaRyeVMOfdaOLirVx1SSHnDQ4luWNVEZk5o5TOXUN45Y1ofkl38+gkB28vbF45f+6bh+iQbKO0VNc5x5F5JjxQxCcLW9Iq3sZfLsrm4gsb07mjvbLNf1500KOrnUWzY9mZ5mTCg0UsfS8OgHsfLmLwoEa8+3osTqem7LAn4DyTJpUw/91mxMdbufTi/Qy+IIyOHas255dfKqVL1xBef7MZ6eluJj1YwsJFVcV20fvRREfXz0DeMDRPP3SAmfNb0DLOyrWX5XP2+Y3ocMT6emNGCald7EybFcnudBdPP1TMrAUtsIfC6wta0DjcgsulufHqfAYOCqPHaaEBZbJYFHf983zumfQeBYUHeW3a9WzYmE7GPv/tcutPmfz70f/WqW+ts9T5VTSst4Ah9fVgvVrEs6fkAPsOOnB5PHz86w4Gt00+Zvth7Tux+JcddepbG1u3uGibZKVNWxt2u+LiyxqxemWFX5v0NDdnDvBuiB2SbWTtMygsMADIzTFY91kFw0c1DijHbzZ/X0GHJBvt2oZgtyuuHhbOJyvK/NpsT3NxzkDv6CQ1xU7GPjd5BQYlBz18ubGCG0Z7Rxp2uyKqaWAXsNmyxUVSkpW2vuVz6bAwVq4s92uTlmYwYKC3CCYn28jMNCjwLZ/69uMWJ62TbCS2sRFiV1x4aSPWrTrs1+bXNDf9fOurXXII2Zlu9hcYKKVoHO59G7rdGrcLlAo8U+eO8WRlF5OT68Dt9vDZ+u0M7F+77TKQvjX5QxYZrfV6oKi+Hi+ucRNyDh2svJ9z6CBxjZvU2DbMauPsxHYs27PrpPvWVl6uQVxC1RsxLt5CXq7/G6RTZxsrl3nfWD987yQ7yyA3xztCePKREu57IBJLPa3d7FyDVglVo4RW8Tayc/zzdO9iZ/Eyb+HZ9H0FezPdZOW42Z3hIqa5hVsmFNJ/cDb/vKeQQ2WBjWRyczwkxFctn/g4K3k5/o/ZuYuN5b7ls+V7J1mZBjm+NkoprhtdxEVDC5n/jn+xrIv8XIO4I/K0jLeSX219dewSwppl3sKzbYuTnCyjcp0ahuaaoXmce1oO/f8SSvfegY1iAGKaNyG/sGq7LCg8SEzziKPade2UwJsv3cBzj15NUpvmJ9W3tv6QRaY2lFJjlVKblVKbjYOHTtD46EnH2sk4v20HNudn4agoP+m+taVreIDqn2633NaEEoeHyy4sYN5bZXTuGoLVBmtXl9O8uYVuPUKOfhAT80wc35QDxR76nZ/FzNkl9Oxmx2ZVuA3Yss3JzddHsnFVAuGNFVNmOALLU8O06nluvS0ch0Mz5IJC5swpo2s3GzZfnfzvh9EsXR7D2/Oa8fbcMr7Z6DQ9z9//GUFJiYdrhuax8K1SUruGYPXVJatV8d6ylqzYGM+PW1yk7wz8qneqxuGQf9Jd6XmMuHEmN93+Fv/9+DuenHRlrfuejD/kMZna0FrPAmYBhCYlHncJ5R4qJT68qlLHh0eQV1bzJRcvbd+ZJb5dpZPtW1tx8VZys6s+CXNzPMS29N/FaBJh4ZnnowDQWnPuWQW0bm3l0yWHWbOqnM/XVlBRoSk96GHiHQeY8mKzOudpFW8lK9tdeT8rx018nH+eyAgLs16IqczTuV8mSW1slB320CreSl/fMYYrLgkPuMjEx1v8RlI5uQaxcf6flxERFqY+37Qyz4AzvcsHIM6XPSbGyoVDQtmyxUW//nbqqmWcldwj8uTlGLSoYX09NiW6Ms9FA3Np1dr/7RfZ1EKfM0PZsK6c5NTAPiQKCg8SG1O1XbaIiaBwv/92WXa4qrh+s/lXrLcOpmlko1r1PRmn7EjmZPxQkEO7yGa0btKUEIuFS9t3YlVG+lHtIkLs9I9LZOUR82rb92R07xnCnj0G+/a6cTo1ny45zHmD/YfQJQ4PTqe3dr634DB9+tlpEmFh4r8i+WJTS9Z+Hcu0l6PoPyA0oAIDcHqvUNJ3u9mz14XTqflg8SEuvsD/eE+xw6jMM+fdUgb2DyMywkJcrI3EBBu70r2fzmu/OEznlMDeQD17hrB7t8Fe3/L5eHE5g6stH8cRy2fBu4fp289ORISFsjIPpaXe3aayMg9frHeSmhrYZ23Xnnb27naTtdeNy6lZ8fFhzh7cyK9NicODy5fn/xYe4vS+oTSJsFC036DE4c1TXq755sty2iUH/tm/Y1cOia2aEdeyKTabhXP/2pkN3/hvl9HNwitvd+oYh0UpHCWHa9X3ZJyyI5mTYWjNw1+t5u2hV2NVFt7btY204v1c26knAPN3/ADAhUkprM/aw2G364R9A2GzKR5+PJKbrivCMODqEY1ISQ1hwTzvbt+oMeH8ku7mvruKsVghOcXGU/+JCug5T5Tn+SejuWx0HoYB149sQpdUO6+/XQLAP66PZGeai5vvLMRqgU4d7bw6tepM19QnorlxfAEulyapjY3XpgV2ythmUzz+eCRjrj2A4YERIxqRmhrCvHne4ytjxjQmPd3NhDsdWK2QkmLjuSneUU1BgYexNxcD4Dbg8svDGHROYMdAbDbFvx6L4p/XF+IxNMOuCSe5Ywjvv+P99B9+XRN2p7uYdPcBrFZon2zjkf94RzWF+QYP3X0Ajwc8Hs0FlzTmr+c1Ot7T1Yrh0bzw6mqmPD4ci0WxdNU29uzdz2VDewGwZNkWzh7QkWEX9cYwPFQ43Tz63JLj9q0rpWva4Q5ySqkFwCAgBsgDJmut3zxW+9CkRB03SX6t4FgSrYEfaKxPhZ7yEzf6HRUZwfdZfMc/b2/oCH7WL73/f1rrPjXNC76lVwta61ENnUEIUTtyTEYIYSopMkIIU0mREUKYSoqMEMJUUmSEEKaSIiOEMJUUGSGEqaTICCFMJUVGCGEqKTJCCFMdt8gopZKVUgNqmP4XpVQH82IJIU4VJxrJvAAcrGH6Yd88IYQ4rhMVmSSt9dbqE7XWm4EkUxIJIU4pJ/oW9vF+tyLwi178TkIzyug4NvCfKakv58+a0NAR/Lx47vyGjuBnYFjgl5+sT39P6t/QEY5iZ1NDR6i1E41kNiml/lF9olLqJuB/5kQSQpxKTjSSuQv4UCl1LVVFpQ9gB64wMZcQ4hRx3CKjtc4DzlJKnQP89iNqn2qtPzM9mRDilFCrK+NprdcCa03OIoQ4Bcl/xhNCmEqKjBDCVFJkhBCmkiIjhDCVFBkhhKmkyAghTCVFRghhKikyQghT/SF/ptYMfS7sxa0v3IjFamHZm2tY9OxHfvPPvKwPNzw2Eu3RGG6DVya8xU8bdpDYMYFJC6u+8BjXPpa5kxfx4fSlAeU5OzGJyf3Pw6oUC3du5dWt/l/wvKX7GQxL7gKATSmSo5rTe/7LOCrKT9i3Lr77vIQ3Hs/GY2gGj4jmqnEt/eaXOty8dP8+cvc6sYcqxj/TmrapjWrVty7WrC3ngYcdeDya60aFc+f4CL/5xcUe7rjnAHsy3ISGKl6c2ozOnUIAcDg83DXxANt3ulEKXpwaxRl9Avs98GDbfoIpk9JaB/I66p1SaggwHbACb2itn6k2vxMwBzgNeFBrPeVEjxmponU/dd4x51ssFubsnM79FzxOYWYRM759mqdGT2fv9szKNmHhYZQf8v4QfLvubZi06G5u6nLXUY+zIPM1bu//b/L3Fh7z+XbN6nvcvBalWDf8Zq5d9h65hw6yZNgY7lj7CWnF+2tsf16bDtzc7XRGLX3vpPvCib+FbRiaW8/fwaNz29M8LoR7r0jjnhfa0jql6kv6bz2dTVi4hZF3xJH5SzmvTc7i8Xc61KpvdQPDjr3sfsvT7y95fLAghoR4K4MvymfWK9GkdgypbDP5cQfh4Yr77o4kLd3FfQ84+PC9GABuu7OI/v1CGTM6HKdTc/iwpmnTYw/qRyaeedw8v/f2Uxu/d6bV+oP/aa371JgloFdSz5RSVuBlYCjQBRillOpSrVkRcAdwwuJSW6l9k8lOzyV3dz5ul5t1izZw1jD/5fXbygDvyqGG4tz7vG7k/JIb8AbSq0U8e0oOsO+gA5fHw8e/7mBw2+Rjth/WvhOLf9lRp761kfZDGfFt7cS1CSXEbmHgJVF8s9rh12Zfejk9zmoCQGKHMPKznBQXumrV92R9972Tdkk2ktrasNsVVwxrzLIV5X5tdu5y8deB3tFJSnII+zLd5BcYHDzo4etvnFw3qjEAdrs6boGpjWDbfoItU1AVGaAvkK61/lVr7QQWAsOObKC1ztdabwLq7aIjMa2iKcis+qQvzCwiplXzo9oNuLwvb/78Ak988m+m3PTqUfMHjRzA2oUbAs4T17gJOYeqLkiYc+ggcY2b1Ng2zGrj7MR2LNuz66T71lZRnouYeHvl/eZxIRTl+S/+pM6N2LjCWzx2/VBGQZaTwhxXrfqerJxcDwkJ1sr7CfFWcnINvzbduoTwydLDgLco7cs0yM4x2JPhpnlzC7dPKOacC/K5c+IBDpV5AsoTbNtPsGUKtiLTCth3xP1M37STppQaq5TarJTa7KLiBG2PnlbTbuSGj77lpi538cgVz3HDYyP85tlCbJx5aR8+f//rusStFqiGPMdoen7bDmzOz8JRUX7SfWurNnvUV90SS6nD4K5LdvLp24W079IIq03Vqm995Km+Du8cH4HDoRk0OJ/XZ5fSvVsINqvCbcDWbS5uvD6ctStjCW+seHFGaUB5gm77CbJMwXbgt4ZFU7f3iNZ6FjALvMdkjte2ILOIFolVVT4mMZr92UXHbL/ti+3Ed4gjsnkEJfu9o4YzhvYi/bvdFOcHtisAkHuolPjwqgOZ8eER5JXV/Ea4tH1nlvh2lU62b201jwuhMMdZeX9/rovoliF+bRpHWLnjuTaAd2Mee/Z2WibaqTjsOWHfk5UQbyE7u2rkkp1jENfS6tcmIsLCS9OaVeY5rX8ebdtYKTusSYi3cvpp3tHVpRc3YvqMmi5jXXvBtv0EW6ZgG8lkAq2PuJ8IZJv9pDs3pdMqJZ64pFhsITYGjRjA10s2+7VJ6BBXeTu5dztC7LbKlQFwzsiBrF34Zb3k+aEgh3aRzWjdpCkhFguXtu/Eqoz0o9pFhNjpH5fIyiPm1bbvyUjp0ZicPU7y9lXgcnr48pNi+p7X1K9NaYmBy+nd7Vi1qIiuZzShcYS1Vn1PVu9edn7d7SZjrxunU/Ph4jKGXOB/INnh8OB0ej9b5r1bxpn97EREWGgZa6VVgpW0dO8u2/ovK/wOGNdFsG0/wZYp2EYym4AUpVQ7IAsYCYw2+0k9hocZt7/J08sfxGK1sGLOWjJ+zuSSWwYD8Mlrq/jLVf04f8zZGC6DisNOnhg5rbJ/aCM7pw/uwQvjZtVLHkNrHv5qNW8PvRqrsvDerm2kFe/n2k49AZi/4wcALkxKYX3WHg67XSfsGwirTfGPya149IZfMTxw/tXRtOkYxvJ3vQcDh4yOITO9nOkT92KxKlonhzH+mcTj9g2EzaZ45okoho8uxOOB0SPC6ZQawpy3DwFw4/Xh7EpzceudB7BaFakdbUyf0qyy/9OPN2Xc7QdwuTRt29h46flmx3qqWgm27SfYMgXjKeyL8P7cihWYrbV+Uik1DkBrPVMpFQdsBiIBD1AKdNFalxzrMU90Cvv3dqJT2L+34LuQeOBnV+rTiU5hi+Ofwg62kQxa66XA0mrTZh5xOxfvbpQQ4g8g2I7JCCFOMVJkhBCmkiIjhDCVFBkhhKmkyAghTCVFRghhKikyQghTSZERQphKiowQwlRSZIQQppIiI4QwlRQZIYSppMgIIUwVdJd6MEOwXepBiFPNH+bXCoQQpx4pMkIIU0mREUKYSoqMEMJUUmSEEKaSIiOEMJUUGSGEqaTICCFMJUVGCGEqKTJCCFNJkRFCmEqKjBDCVEH3M7UNpc+Fvbj1hRuxWC0se3MNi579yG/+mZf14YbHRqI9GsNt8MqEt/hpww4SOyYwaeGEynZx7WOZO3kRH05fSiAkj+QJVLBkapBvYSulhgDTASvwhtb6mWrzlW/+RUAZcIPW+jvfvNnAJUC+1rpbbZ7vRN/CtlgszNk5nfsveJzCzCJmfPs0T42ezt7tmZVtwsLDKD9UDkC77m2YtOhubupy11GPsyDzNW7v/2/y99b9R+Mlj+QJJE9DZAqqb2ErpazAy8BQoAswSinVpVqzoUCK799Y4NUj5r0FDKnPTKl9k8lOzyV3dz5ul5t1izZw1jD/5fXbygDvyqGG4tz7vG7k/JIb8AYieSRPoIIpU0PsLvUF0rXWvwIopRYCw4Cfj2gzDHhbe4dZG5VSUUqpeK11jtZ6vVIqqT4DxbSKpiBzf+X9wswiOvVLOardgMv78venRhMV25RJlzx91PxBIwewduEGySN5GjRPsGVqiAO/rYB9R9zP9E072TbHpZQaq5TarJTa7KLiBG2PnlbTbuSGj77lpi538cgVz3HDYyP85tlCbJx5aR8+f//rk4kpeSRPvecJtkwNUWRqePlUf/W1aXNcWutZWus+Wus+IYQet21BZhEtEptX3o9JjGZ/dtEx22/7YjvxHeKIbB5ROe2Mob1I/243xfmOk4kpeSRPvecJtkwNUWQygdZH3E8EsuvQpt7s3JROq5R44pJisYXYGDRiAF8v2ezXJqFDXOXt5N7tCLHbKNl/sHLaOSMHsnbhl5JH8jR4nmDL1BDHZDYBKUqpdkAWMBIYXa3NEmC873hNP8Chtc4xK5DH8DDj9jd5evmDWKwWVsxZS8bPmVxyy2AAPnltFX+5qh/njzkbw2VQcdjJEyOnVfYPbWTn9ME9eGHcLMkjeRo8T7BlaqhT2BcBL+A9hT1ba/2kUmocgNZ6pu8U9gy8Z5HKgBu11pt9fRcAg4AYIA+YrLV+83jPJxcSF8JcxzuFLb9WIIQIWFD9PxkhxJ+LFBkhhKmkyAghTCVFRghhKikyQghTSZERQphKiowQwlRSZIQQppIiI4QwlRQZIYSppMgIIUwlRUYIYSopMkIIU0mREUKYSoqMEMJUUmSEEKaSIiOEMJUUGSGEqaTICCFMJUVGCGEqKTJCCFNJkRFCmEqKjBDCVFJkhBCmkiIjhDCVFBkhhKmkyAghTGVr6ADBos+Fvbj1hRuxWC0se3MNi579yG/+mZf14YbHRqI9GsNt8MqEt/hpww4SOyYwaeGEynZx7WOZO3kRH05fKnkkT4PlCaZMSmsdyOs4/oMrNQSYDliBN7TWz1Sbr3zzLwLKgBu01t8dr69SajjwCNAZ6Ku13nyiHJEqWvdT5x1zvsViYc7O6dx/weMUZhYx49uneWr0dPZuz6xsExYeRvmhcgDadW/DpEV3c1OXu456nAWZr3F7/3+Tv7fwRLEkj+QxJU9DZFqtP/if1rpPjVkCeiXHoZSyAi8DQ4EuwCilVJdqzYYCKb5/Y4FXa9H3R+BKYH19ZU3tm0x2ei65u/Nxu9ysW7SBs4b5L6/fVgZ4Vw41FOfe53Uj55fcgDcQySN5AhVMmczcXeoLpGutfwVQSi0EhgE/H9FmGPC29g6nNiqlopRS8UDSsfpqrbf7ptVb0JhW0RRk7q+8X5hZRKd+KUe1G3B5X/7+1GiiYpsy6ZKnj5o/aOQA1i7cIHkkT4PmCbZMZh74bQXsO+J+pm9abdrUpu9xKaXGKqU2K6U2u6g4Qdujp9W0G7nho2+5qctdPHLFc9zw2Ai/ebYQG2de2ofP3//6ZGJKHslT73mCLZOZRaamoUb1V3msNrXpe1xa61la6z5a6z4hhB63bUFmES0Sm1fej0mMZn920THbb/tiO/Ed4ohsHlE57YyhvUj/bjfF+Y6TiSl5JE+95wm2TGYWmUyg9RH3E4HsWrapTd96s3NTOq1S4olLisUWYmPQiAF8vcT/eHJCh7jK28m92xFit1Gy/2DltHNGDmTtwi8lj+Rp8DzBlsnMYzKbgBSlVDsgCxgJjK7WZgkw3nfMpR/g0FrnKKUKatG33ngMDzNuf5Onlz+IxWphxZy1ZPycySW3DAbgk9dW8Zer+nH+mLMxXAYVh508MXJaZf/QRnZOH9yDF8bNkjySp8HzBFsms09hXwS8gPc09Gyt9ZNKqXEAWuuZvlPYM4AheE9h3/jbKema+vqmXwG8BLQAioEtWusLj5fjRKewhRCBOd4pbFOLTLCQIiOEuRrk/8kIIQRIkRFCmEyKjBDCVFJkhBCmkiIjhDCVFBkhhKmkyAghTCVFRghhKikyQghTSZERQphKiowQwlRSZIQQpvpTfEHSd+mIjHp4qBgg8Auw1p9gywPBl0nyHF995WmrtW5R04w/RZGpL0qpzcf6pmlDCLY8EHyZJM/x/R55ZHdJCGEqKTJCCFNJkTk59Xd9xPoRbHkg+DJJnuMzPY8ckxFCmEpGMkIIU0mREUKY6k9bZJRSQ5RSO5VS6Uqpf9UwXymlXvTN36qUOu1EfZVSw5VSPymlPEqpgE4LBphvtlIqXyn1YyAZAsjWSSn1tVKqQik10YwMtcho6jKoawalVLRSapVSKs33t1kwZVBK/du3XncqpY77KyC1prX+0/3D+zMrvwDtATvwA9ClWpuLgGV4f82yP/DNifoCnYFUYB3QpyHy+eb9FTgN+LGBll0scAbwJDCxgdaxacsgkAzAc8C/fLf/BTwbLBmALr71GQq0861na6AZ/qwjmb5Autb6V621E1gIDKvWZhjwtvbaCEQppeKP11drvV1rvbOB86G1Xg8c+zdJTc6mtc7XWm8CXCZlOCGTl0EgGYYBc3235wKXB1GGYcBCrXWF1no3kI53fQfkz1pkWgH7jrif6ZtWmza16duQ+czWUM97qmiptc4B8P2NDaIMpqzbP2uRUTVMq34u/1htatM3UIHkM1tDPa8wnynr9s9aZDKB1kfcTwSya9mmNn0bMp/ZGup5TxV5v+3W+v7mB1EGU9btn7XIbAJSlFLtlFJ2YCSwpFqbJcD1vrM4/QGHb2hZm74Nmc9sv8frP5UtAf7mu/03YHEQZVgCjFRKhSql2gEpwLcBP1tDHXlv6H94z87swnsE/UHftHHAON9tBbzsm7+NI84W1dTXN/0KvJ8GFUAesKKB8i0AcvAeeM0Ebvqdl12c73lLgGLf7cjfef2augzqmgFoDqwB0nx/o4MpA/Cgb73uBIbWRwb5WoEQwlR/1t0lIcTvRIqMEMJUUmSEEKaSIiOEMJUUGSGEqaTICCFMJUVGCGEqW0MHEKcWpdRDwLV4v2hXCPwPcABj8V4aIh0Yo7UuU0q9BRwGOgFtgRvx/g/UM/FeuuIG32OW4v2Ph+cDB4AH8F6uoA1wl9Z6iVIqCZgHhPuijNdaf2XyyxW1ICMZUW98F+q6CugNXAn8duGu/9Nan6G17glsx/u/Tn/TDDgXmAB8DEwDugLdlVK9fG3CgXVa69OBg8ATwGC8/8P6MV+bfGCw1vo0YATwohmvUZw8GcmI+jQQWKy1PgyglPrYN72bUuoJIApoAqw4os/HWmutlNoG5Gmtt/n6/gQkAVsAJ7Dc134bUKG1dvn6JPmmhwAzfIXJADqa8PpEHUiREfWppksFALwFXK61/kEpdQMw6Ih5Fb6/niNu/3b/t+3Tpau+/1LZTmvtUUr91mYC3u+L9cQ7Qi+v86sQ9Up2l0R9+hK4VCkVppRqAlzsmx4B5CilQvAerzFDUyBHa+0BxuC9TKgIAjKSEfVGa71JKbUE73ViM4DNeA/6PgR845u2DW/RqW+vAP9VSg0H1gKHTHgOUQfyLWxRr5RSTbTWpUqpxsB6YKzW+ruGziUajoxkRH2bpZTqAoQBc6XACBnJCCFMJQd+hRCmkiIjhDCVFBkhhKmkyAghTCVFRghhqv8HycWJQAkK738AAAAASUVORK5CYII=",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores = np.array(results.mean_test_score).reshape(6, 6)\n",
    "\n",
    "# plot the mean cross-validation scores\n",
    "mglearn.tools.heatmap(scores, xlabel='gamma', xticklabels=param_grid['gamma'],\n",
    "                      ylabel='C', yticklabels=param_grid['C'], cmap=\"viridis\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Each point in the heat map corresponds to one run of cross-validation, with a particular parameter setting. The color encodes the cross-validation accuracy, with light colors meaning high accuracy and dark colors meaning low accuracy. <br><br>\n",
    "- You can see that SVC is very sensitive to the setting of the parameters. For many of the parameter settings, the accuracy is around 40%, which is quite bad; for other settings the accuracy is around 96%. <br><br>\n",
    "- We can take away from this plot several things. First, the parameters we adjusted are very important for obtaining good performance.  <br><br>\n",
    "- Both parameters (C and gamma) matter a lot, as adjusting them can change the accuracy from 40% to 96%.    <br><br>\n",
    "- Additionally, the ranges we picked for the parameters are ranges in which we see significant changes in the outcome. It’s also important to note that the ranges for the parameters are large enough: the optimum values for each parameter are not on the edges of the plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.colorbar.Colorbar at 0x20b5cc25ec8>"
      ]
     },
     "execution_count": 35,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 936x360 with 4 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, axes = plt.subplots(1, 3, figsize=(13, 5))\n",
    "\n",
    "param_grid_linear = {'C': np.linspace(1, 2, 6),\n",
    "                     'gamma':  np.linspace(1, 2, 6)}\n",
    "\n",
    "param_grid_one_log = {'C': np.linspace(1, 2, 6),\n",
    "                      'gamma':  np.logspace(-3, 2, 6)}\n",
    "\n",
    "param_grid_range = {'C': np.logspace(-3, 2, 6),\n",
    "                    'gamma':  np.logspace(-7, -2, 6)}\n",
    "\n",
    "for param_grid, ax in zip([param_grid_linear, param_grid_one_log,\n",
    "                           param_grid_range], axes):\n",
    "    grid_search = GridSearchCV(SVC(), param_grid, cv=5)\n",
    "    grid_search.fit(X_train, y_train)\n",
    "    scores = grid_search.cv_results_['mean_test_score'].reshape(6, 6)\n",
    "\n",
    "    # plot the mean cross-validation scores\n",
    "    scores_image = mglearn.tools.heatmap(\n",
    "        scores, xlabel='gamma', ylabel='C', xticklabels=param_grid['gamma'],\n",
    "        yticklabels=param_grid['C'], cmap=\"viridis\", ax=ax)\n",
    "\n",
    "plt.colorbar(scores_image, ax=axes.tolist())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "List of grids:\n",
      "[{'kernel': ['rbf'], 'C': [0.001, 0.01, 0.1, 1, 10, 100], 'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}, {'kernel': ['linear'], 'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n"
     ]
    }
   ],
   "source": [
    "param_grid = [{'kernel': ['rbf'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "               'gamma': [0.001, 0.01, 0.1, 1, 10, 100]},\n",
    "              {'kernel': ['linear'],\n",
    "               'C': [0.001, 0.01, 0.1, 1, 10, 100]}]\n",
    "print(\"List of grids:\\n{}\".format(param_grid))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best parameters: {'C': 10, 'gamma': 0.1, 'kernel': 'rbf'}\n",
      "Best cross-validation score: 0.97\n"
     ]
    }
   ],
   "source": [
    "grid_search = GridSearchCV(SVC(), param_grid, cv=5,\n",
    "                          return_train_score=True)\n",
    "grid_search.fit(X_train, y_train)\n",
    "print(\"Best parameters: {}\".format(grid_search.best_params_))\n",
    "print(\"Best cross-validation score: {:.2f}\".format(grid_search.best_score_))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>0</th>\n",
       "      <th>1</th>\n",
       "      <th>2</th>\n",
       "      <th>3</th>\n",
       "      <th>...</th>\n",
       "      <th>38</th>\n",
       "      <th>39</th>\n",
       "      <th>40</th>\n",
       "      <th>41</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>mean_fit_time</th>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0006</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.001</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_fit_time</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.00049</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>...</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0.00041</td>\n",
       "      <td>1.6e-05</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_score_time</th>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>0.0002</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0008</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_score_time</th>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.00049</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0004</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "      <td>0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_C</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>0.001</td>\n",
       "      <td>...</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>10</td>\n",
       "      <td>100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_gamma</th>\n",
       "      <td>0.001</td>\n",
       "      <td>0.01</td>\n",
       "      <td>0.1</td>\n",
       "      <td>1</td>\n",
       "      <td>...</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "      <td>NaN</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>param_kernel</th>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>rbf</td>\n",
       "      <td>...</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "      <td>linear</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>params</th>\n",
       "      <td>{'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}</td>\n",
       "      <td>{'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}</td>\n",
       "      <td>...</td>\n",
       "      <td>{'C': 0.1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 1, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 10, 'kernel': 'linear'}</td>\n",
       "      <td>{'C': 100, 'kernel': 'linear'}</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_test_score</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_test_score</th>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>0.35</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.96</td>\n",
       "      <td>1</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_test_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.91</td>\n",
       "      <td>0.91</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_test_score</th>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>0.41</td>\n",
       "      <td>...</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "      <td>0.95</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_test_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.96</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_test_score</th>\n",
       "      <td>0.022</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.022</td>\n",
       "      <td>...</td>\n",
       "      <td>0.04</td>\n",
       "      <td>0.022</td>\n",
       "      <td>0.036</td>\n",
       "      <td>0.029</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>rank_test_score</th>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>27</td>\n",
       "      <td>...</td>\n",
       "      <td>8</td>\n",
       "      <td>1</td>\n",
       "      <td>3</td>\n",
       "      <td>8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split0_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.98</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split1_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split2_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.94</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split3_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>split4_train_score</th>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>0.36</td>\n",
       "      <td>...</td>\n",
       "      <td>0.97</td>\n",
       "      <td>0.99</td>\n",
       "      <td>1</td>\n",
       "      <td>1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>mean_train_score</th>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>0.37</td>\n",
       "      <td>...</td>\n",
       "      <td>0.96</td>\n",
       "      <td>0.98</td>\n",
       "      <td>0.99</td>\n",
       "      <td>0.99</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>std_train_score</th>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>0.0056</td>\n",
       "      <td>...</td>\n",
       "      <td>0.0088</td>\n",
       "      <td>0.0055</td>\n",
       "      <td>0.007</td>\n",
       "      <td>0.0084</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "<p>23 rows × 42 columns</p>\n",
       "</div>"
      ],
      "text/plain": [
       "                                                               0   \\\n",
       "mean_fit_time                                              0.0008   \n",
       "std_fit_time                                               0.0004   \n",
       "mean_score_time                                            0.0002   \n",
       "std_score_time                                             0.0004   \n",
       "param_C                                                     0.001   \n",
       "param_gamma                                                 0.001   \n",
       "param_kernel                                                  rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.001, 'kernel': 'rbf'}   \n",
       "split0_test_score                                            0.35   \n",
       "split1_test_score                                            0.35   \n",
       "split2_test_score                                            0.36   \n",
       "split3_test_score                                            0.36   \n",
       "split4_test_score                                            0.41   \n",
       "mean_test_score                                              0.37   \n",
       "std_test_score                                              0.022   \n",
       "rank_test_score                                                27   \n",
       "split0_train_score                                           0.37   \n",
       "split1_train_score                                           0.37   \n",
       "split2_train_score                                           0.37   \n",
       "split3_train_score                                           0.37   \n",
       "split4_train_score                                           0.36   \n",
       "mean_train_score                                             0.37   \n",
       "std_train_score                                            0.0056   \n",
       "\n",
       "                                                              1   \\\n",
       "mean_fit_time                                             0.0006   \n",
       "std_fit_time                                             0.00049   \n",
       "mean_score_time                                           0.0004   \n",
       "std_score_time                                           0.00049   \n",
       "param_C                                                    0.001   \n",
       "param_gamma                                                 0.01   \n",
       "param_kernel                                                 rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.01, 'kernel': 'rbf'}   \n",
       "split0_test_score                                           0.35   \n",
       "split1_test_score                                           0.35   \n",
       "split2_test_score                                           0.36   \n",
       "split3_test_score                                           0.36   \n",
       "split4_test_score                                           0.41   \n",
       "mean_test_score                                             0.37   \n",
       "std_test_score                                             0.022   \n",
       "rank_test_score                                               27   \n",
       "split0_train_score                                          0.37   \n",
       "split1_train_score                                          0.37   \n",
       "split2_train_score                                          0.37   \n",
       "split3_train_score                                          0.37   \n",
       "split4_train_score                                          0.36   \n",
       "mean_train_score                                            0.37   \n",
       "std_train_score                                           0.0056   \n",
       "\n",
       "                                                             2   \\\n",
       "mean_fit_time                                            0.0008   \n",
       "std_fit_time                                             0.0004   \n",
       "mean_score_time                                          0.0002   \n",
       "std_score_time                                           0.0004   \n",
       "param_C                                                   0.001   \n",
       "param_gamma                                                 0.1   \n",
       "param_kernel                                                rbf   \n",
       "params              {'C': 0.001, 'gamma': 0.1, 'kernel': 'rbf'}   \n",
       "split0_test_score                                          0.35   \n",
       "split1_test_score                                          0.35   \n",
       "split2_test_score                                          0.36   \n",
       "split3_test_score                                          0.36   \n",
       "split4_test_score                                          0.41   \n",
       "mean_test_score                                            0.37   \n",
       "std_test_score                                            0.022   \n",
       "rank_test_score                                              27   \n",
       "split0_train_score                                         0.37   \n",
       "split1_train_score                                         0.37   \n",
       "split2_train_score                                         0.37   \n",
       "split3_train_score                                         0.37   \n",
       "split4_train_score                                         0.36   \n",
       "mean_train_score                                           0.37   \n",
       "std_train_score                                          0.0056   \n",
       "\n",
       "                                                           3   ...  \\\n",
       "mean_fit_time                                          0.0008  ...   \n",
       "std_fit_time                                           0.0004  ...   \n",
       "mean_score_time                                        0.0002  ...   \n",
       "std_score_time                                         0.0004  ...   \n",
       "param_C                                                 0.001  ...   \n",
       "param_gamma                                                 1  ...   \n",
       "param_kernel                                              rbf  ...   \n",
       "params              {'C': 0.001, 'gamma': 1, 'kernel': 'rbf'}  ...   \n",
       "split0_test_score                                        0.35  ...   \n",
       "split1_test_score                                        0.35  ...   \n",
       "split2_test_score                                        0.36  ...   \n",
       "split3_test_score                                        0.36  ...   \n",
       "split4_test_score                                        0.41  ...   \n",
       "mean_test_score                                          0.37  ...   \n",
       "std_test_score                                          0.022  ...   \n",
       "rank_test_score                                            27  ...   \n",
       "split0_train_score                                       0.37  ...   \n",
       "split1_train_score                                       0.37  ...   \n",
       "split2_train_score                                       0.37  ...   \n",
       "split3_train_score                                       0.37  ...   \n",
       "split4_train_score                                       0.36  ...   \n",
       "mean_train_score                                         0.37  ...   \n",
       "std_train_score                                        0.0056  ...   \n",
       "\n",
       "                                                38  \\\n",
       "mean_fit_time                                    0   \n",
       "std_fit_time                                     0   \n",
       "mean_score_time                             0.0008   \n",
       "std_score_time                              0.0004   \n",
       "param_C                                        0.1   \n",
       "param_gamma                                    NaN   \n",
       "param_kernel                                linear   \n",
       "params              {'C': 0.1, 'kernel': 'linear'}   \n",
       "split0_test_score                                1   \n",
       "split1_test_score                             0.91   \n",
       "split2_test_score                                1   \n",
       "split3_test_score                             0.91   \n",
       "split4_test_score                             0.95   \n",
       "mean_test_score                               0.96   \n",
       "std_test_score                                0.04   \n",
       "rank_test_score                                  8   \n",
       "split0_train_score                            0.97   \n",
       "split1_train_score                            0.97   \n",
       "split2_train_score                            0.94   \n",
       "split3_train_score                            0.97   \n",
       "split4_train_score                            0.97   \n",
       "mean_train_score                              0.96   \n",
       "std_train_score                             0.0088   \n",
       "\n",
       "                                              39  \\\n",
       "mean_fit_time                                  0   \n",
       "std_fit_time                                   0   \n",
       "mean_score_time                                0   \n",
       "std_score_time                                 0   \n",
       "param_C                                        1   \n",
       "param_gamma                                  NaN   \n",
       "param_kernel                              linear   \n",
       "params              {'C': 1, 'kernel': 'linear'}   \n",
       "split0_test_score                              1   \n",
       "split1_test_score                           0.96   \n",
       "split2_test_score                              1   \n",
       "split3_test_score                           0.95   \n",
       "split4_test_score                           0.95   \n",
       "mean_test_score                             0.97   \n",
       "std_test_score                             0.022   \n",
       "rank_test_score                                1   \n",
       "split0_train_score                          0.99   \n",
       "split1_train_score                          0.98   \n",
       "split2_train_score                          0.98   \n",
       "split3_train_score                          0.99   \n",
       "split4_train_score                          0.99   \n",
       "mean_train_score                            0.98   \n",
       "std_train_score                           0.0055   \n",
       "\n",
       "                                               40  \\\n",
       "mean_fit_time                              0.0002   \n",
       "std_fit_time                              0.00041   \n",
       "mean_score_time                                 0   \n",
       "std_score_time                                  0   \n",
       "param_C                                        10   \n",
       "param_gamma                                   NaN   \n",
       "param_kernel                               linear   \n",
       "params              {'C': 10, 'kernel': 'linear'}   \n",
       "split0_test_score                               1   \n",
       "split1_test_score                               1   \n",
       "split2_test_score                               1   \n",
       "split3_test_score                            0.91   \n",
       "split4_test_score                            0.95   \n",
       "mean_test_score                              0.97   \n",
       "std_test_score                              0.036   \n",
       "rank_test_score                                 3   \n",
       "split0_train_score                           0.99   \n",
       "split1_train_score                           0.99   \n",
       "split2_train_score                           0.98   \n",
       "split3_train_score                           0.99   \n",
       "split4_train_score                              1   \n",
       "mean_train_score                             0.99   \n",
       "std_train_score                             0.007   \n",
       "\n",
       "                                                41  \n",
       "mean_fit_time                                0.001  \n",
       "std_fit_time                               1.6e-05  \n",
       "mean_score_time                                  0  \n",
       "std_score_time                                   0  \n",
       "param_C                                        100  \n",
       "param_gamma                                    NaN  \n",
       "param_kernel                                linear  \n",
       "params              {'C': 100, 'kernel': 'linear'}  \n",
       "split0_test_score                             0.96  \n",
       "split1_test_score                             0.96  \n",
       "split2_test_score                                1  \n",
       "split3_test_score                             0.91  \n",
       "split4_test_score                             0.95  \n",
       "mean_test_score                               0.96  \n",
       "std_test_score                               0.029  \n",
       "rank_test_score                                  8  \n",
       "split0_train_score                            0.98  \n",
       "split1_train_score                            0.99  \n",
       "split2_train_score                            0.99  \n",
       "split3_train_score                               1  \n",
       "split4_train_score                               1  \n",
       "mean_train_score                              0.99  \n",
       "std_train_score                             0.0084  \n",
       "\n",
       "[23 rows x 42 columns]"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "results = pd.DataFrame(grid_search.cv_results_)\n",
    "# we display the transposed table so that it better fits on the page:\n",
    "display(results.T)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Using different cross-validation strategies with grid search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Nested cross-validation"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Cross-validation scores:  [0.967 1.    0.967 0.967 1.   ]\n",
      "Mean cross-validation score:  0.9800000000000001\n"
     ]
    }
   ],
   "source": [
    "param_grid = {'C': [0.001, 0.01, 0.1, 1, 10, 100],\n",
    "              'gamma': [0.001, 0.01, 0.1, 1, 10, 100]}\n",
    "scores = cross_val_score(GridSearchCV(SVC(), param_grid, cv=5),\n",
    "                         iris.data, iris.target, cv=5)\n",
    "print(\"Cross-validation scores: \", scores)\n",
    "print(\"Mean cross-validation score: \", scores.mean())"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "def nested_cv(X, y, inner_cv, outer_cv, Classifier, parameter_grid):\n",
    "    outer_scores = []\n",
    "    # for each split of the data in the outer cross-validation\n",
    "    # (split method returns indices of training and test parts)\n",
    "    for training_samples, test_samples in outer_cv.split(X, y):\n",
    "        # find best parameter using inner cross-validation\n",
    "        best_parms = {}\n",
    "        best_score = -np.inf\n",
    "        # iterate over parameters\n",
    "        for parameters in parameter_grid:\n",
    "            # accumulate score over inner splits\n",
    "            cv_scores = []\n",
    "            # iterate over inner cross-validation\n",
    "            for inner_train, inner_test in inner_cv.split(\n",
    "                    X[training_samples], y[training_samples]):\n",
    "                # build classifier given parameters and training data\n",
    "                clf = Classifier(**parameters)\n",
    "                clf.fit(X[inner_train], y[inner_train])\n",
    "                # evaluate on inner test set\n",
    "                score = clf.score(X[inner_test], y[inner_test])\n",
    "                cv_scores.append(score)\n",
    "            # compute mean score over inner folds\n",
    "            mean_score = np.mean(cv_scores)\n",
    "            if mean_score > best_score:\n",
    "                # if better than so far, remember parameters\n",
    "                best_score = mean_score\n",
    "                best_params = parameters\n",
    "        # build classifier on best parameters using outer training set\n",
    "        clf = Classifier(**best_params)\n",
    "        clf.fit(X[training_samples], y[training_samples])\n",
    "        # evaluate\n",
    "        outer_scores.append(clf.score(X[test_samples], y[test_samples]))\n",
    "    return np.array(outer_scores)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "ename": "NameError",
     "evalue": "name 'iris' is not defined",
     "output_type": "error",
     "traceback": [
      "\u001b[0;31m---------------------------------------------------------------------------\u001b[0m",
      "\u001b[0;31mNameError\u001b[0m                                 Traceback (most recent call last)",
      "\u001b[0;32m/var/folders/l_/499_8k_5387b9tz3wp5g8lg00000gn/T/ipykernel_98063/1517725188.py\u001b[0m in \u001b[0;36m<module>\u001b[0;34m\u001b[0m\n\u001b[1;32m      1\u001b[0m \u001b[0;32mfrom\u001b[0m \u001b[0msklearn\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mmodel_selection\u001b[0m \u001b[0;32mimport\u001b[0m \u001b[0mParameterGrid\u001b[0m\u001b[0;34m,\u001b[0m \u001b[0mStratifiedKFold\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n\u001b[0;32m----> 2\u001b[0;31m scores = nested_cv(iris.data, iris.target, StratifiedKFold(5),\n\u001b[0m\u001b[1;32m      3\u001b[0m                    StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n\u001b[1;32m      4\u001b[0m \u001b[0mprint\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0;34m\"Cross-validation scores: {}\"\u001b[0m\u001b[0;34m.\u001b[0m\u001b[0mformat\u001b[0m\u001b[0;34m(\u001b[0m\u001b[0mscores\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m)\u001b[0m\u001b[0;34m\u001b[0m\u001b[0;34m\u001b[0m\u001b[0m\n",
      "\u001b[0;31mNameError\u001b[0m: name 'iris' is not defined"
     ]
    }
   ],
   "source": [
    "from sklearn.model_selection import ParameterGrid, StratifiedKFold\n",
    "scores = nested_cv(iris.data, iris.target, StratifiedKFold(5),\n",
    "                   StratifiedKFold(5), SVC, ParameterGrid(param_grid))\n",
    "print(\"Cross-validation scores: {}\".format(scores))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Parallelizing cross-validation and grid search"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Evaluation Metrics and Scoring\n",
    "\n",
    "- we have evaluated classification performance using accuracy  and regression performance using R2. \n",
    "\n",
    "- In practice, these evaluation metrics might not be appropriate for your application, and it is important to choose the right metric when selecting between models and adjusting parameters\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/accuracy.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Example: Imagine we have a sample of 100 cases, 90 healthy and the others patient. If a test can be positive for all healthuy and be negative for 5 the healthy ones, it is 100% accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 90,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.95"
      ]
     },
     "execution_count": 90,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy = (90+5)/100\n",
    "Accuracy    # 100percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- When selecting a metric, you should always have the end goal of the machine learning application in mind <br><br>\n",
    "\n",
    "- measuring accuracy might be misleading <br><br>\n",
    "- We will first discuss metrics for the important special case of binary classification, then turn to multiclass classification and finally regression\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics for Binary Classification\n",
    "\n",
    "- Binary classification is arguably the most common and conceptually simple application of machine learning in practice. However, there are still a number of caveats in evaluating even this simple task.\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Kinds of errors : False Positive and False Negative\n",
    "\n",
    "- Often, accuracy is not a good measure of predictive performance.  <br><br>\n",
    "- Example, imagine an application to screen for the early detection of cancer using an automated test. <br><br>\n",
    "- If the test is negative, the patient will be assumed healthy, while if the test is positive, the patient will undergo additional screening. Here, we would call a positive test (an indication of cancer) the positive class, and a negative test the negative class.  <br><br>\n",
    "- We can’t assume that our model will always work perfectly, and it will make mistakes. For any application, we need to ask ourselves what the consequences of these mistakes might be in the real world\n",
    "\n",
    ">  **First mistake** is that a healthy patient will be classified as positive, leading to additional testing. This leads to some costs and an inconvenience for the patient (and possibly some mental distress). An incorrect positive prediction is called a false positive. \n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Second mistake** is that a sick patient will be classified as negative, and will not receive further tests and treatment. The undiagnosed cancer might lead to serious health issues, and could even be fatal. A mistake of this kind—an incorrect negative prediction—is called a false negative. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- In statistics, a false positive is also known as type I error, and a false negative as type II error. We will stick to “false negative” and “false positive,” as they are more explicit and easier to remember. <br><br>\n",
    "- In the cancer diagnosis example, it is clear that we want to avoid false negatives as much as possible, while false positives can be viewed as more of a minor nuisance. While this is a particularly drastic example, the consequence of false positives and false negatives are rarely the same.  <br><br>\n",
    "- In commercial applications, it might be possible to assign dollar values to both kinds of mistakes, which would allow measuring the error of a particular prediction in dollars, instead of accuracy. This might be much more meaningful for making business decisions on which model to use"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Imbalanced datasets"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Datasets in which one class is much more frequent than the other are often called imbalanced datasets, or datasets with imbalanced classes. <br><br>\n",
    "- In reality, imbalanced data is the norm, and it is rare that the events of interest have equal or even similar frequency in the data.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Example: Imagine we have a sample of 100 cases, 50 healthy and the others patient. If a test can be positive for all patients and be negative for all the healthy ones, it is 100% accurate."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 14,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy = (50+50)/100\n",
    "Accuracy    # 100percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Example: Imagine we have a sample of 100 cases, 90 healthy and the others patient. If a test can be positive for all healthy and be negative for 0 patient, what is the accuracy here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 92,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 92,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy = (90+0)/100\n",
    "Accuracy    # 100percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "Accuracy = (90+0)/100\n",
    "Accuracy    # 100percent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Lets creates an  imbalanced dataset from the digits dataset, by classifying the digit 9 against the nine other classes:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 95,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "digits = load_digits()\n",
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
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     },
     "execution_count": 37,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
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    "pd.DataFrame(digits.data).head()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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    "pd.DataFrame(digits.target).head()"
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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   "source": [
    "pd.DataFrame(y).head()"
   ]
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  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, ..., False,  True, False])"
      ]
     },
     "execution_count": 29,
     "metadata": {},
     "output_type": "execute_result"
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   "source": [
    "y"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Baseline Performance"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> To be able to truly understand and then improve our model’s performance, we first need to establish a baseline for the data that we have. After all, if we don’t know how our model is performing against a baseline, how can we improve it?\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> “If you can not measure it, you can not improve it” — Lord Kelvin"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "This is why establishing a baseline model that we can compare our model’s performance to is so important, and this is where the Dummy Classifier comes into play"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Dummy Classifier\n",
    "\n",
    "- A dummy classifier is exactly what it sounds like! It is a classifier model that makes predictions without trying to find patterns in the data and classifies the given data using only simple rules. <br><br>\n",
    "- The classifier’s behavior is completely independent of the training data as the trends in the training data are completely ignored and instead uses one of the strategies to predict the class label. <br><br>\n",
    "- It is used only as a simple baseline for the other classifiers i.e. any other classifier is expected to perform better on the given dataset. It is especially useful for datasets where are sure of a class imbalance. It is based on the philosophy that any analytic approach for a classification problem should be better than a random guessing approach."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 96,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Unique predicted labels: [False]\n",
      "Test score: 0.90\n"
     ]
    }
   ],
   "source": [
    "# We can use the DummyClassifier to always predict the majority class (here “not nine”) to see how uninformative accuracy can be:\n",
    "from sklearn.dummy import DummyClassifier\n",
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "print(\"Unique predicted labels: {}\".format(np.unique(pred_most_frequent)))\n",
    "print(\"Test score: {:.2f}\".format(dummy_majority.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False,\n",
       "       False, False, False, False, False, False, False, False, False])"
      ]
     },
     "execution_count": 40,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "pred_most_frequent"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> We obtained close to 90% accuracy without learning anything. This might seem striking, but think about it for a minute. Imagine someone telling you their model is 90% accurate. You might think they did a very good job. But depending on the problem, that might be possible by just predicting one class! Let’s compare this against using an actual classifier:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 97,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Test score: 0.92\n"
     ]
    }
   ],
   "source": [
    "from sklearn.tree import DecisionTreeClassifier\n",
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "print(\"Test score: {:.2f}\".format(tree.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> According to accuracy, the DecisionTreeClassifier is only slightly better than the constant predictor. This could indicate either that something is wrong with how we used DecisionTreeClassifier, or that accuracy is in fact not a good measure here. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- The problem here is that accuracy is an inadequate measure for quantifying predictive performance in this imbalanced setting. <br><br>\n",
    "- We will explore alternative metrics that provide better guidance in selecting models.<br><br>\n",
    "- In particular, we would like to have metrics that tell us how much better a model is than making “most frequent” predictions or random predictions, as they are computed in pred_most_frequent and pred_dummy. <br><br>\n",
    "- If we use a metric to assess our models, it should definitely be able to weed out these nonsense predictions\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Confusion matrices\n",
    "\n",
    "- One of the most comprehensive ways to represent the result of evaluating binary classification is using confusion matrices <br><br>\n",
    "- Let us use the same example of digits\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 98,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "logreg score: 0.98\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(C=0.01).fit(X_train, y_train) \n",
    "pred_logreg = logreg.predict(X_test) \n",
    "print(\"logreg score: {:.2f}\".format(logreg.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 99,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Confusion matrix:\n",
      "[[402   1]\n",
      " [  7  40]]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "\n",
    "confusion = confusion_matrix(y_test, pred_logreg) # (y_true, y_pred)\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The output of confusion_matrix is a two-by-two array, where the rows correspond to the true classes and the columns correspond to the predicted classes.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/confusion_matrix_sklearn_rep.png)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    " Each entry counts how often a sample that belongs to the class corresponding to the row (here, “not nine” and “nine”) was classified as the class corresponding to the column."
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 576x576 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_confusion_matrix_illustration()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Entries on the main diagonal3 of the confusion matrix correspond to correct classifications, while other entries tell us how many samples of one class got mistakenly classified as another class\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Example: Imagine we have a sample of 100 cases, 90 healthy and the others patient. If a test can be positive for all healthy and be negative for 0 patient, what is the accuracy here?"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "Accuracy = (90+0)/100\n",
    "Accuracy    # 100percent"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_binary_confusion_matrix()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Now let’s use the confusion matrix to compare the models we fitted earlier (the two dummy models, the decision tree, and the logistic regression):\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 100,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Most frequent class:\n",
      "[[403   0]\n",
      " [ 47   0]]\n"
     ]
    }
   ],
   "source": [
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "\n",
    "print(\"Most frequent class:\")\n",
    "print(confusion_matrix(y_test, pred_most_frequent))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- Looking at the confusion matrix, it is quite clear that something is wrong with pred_most_frequent, because it always predicts the same class.\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Decision tree:\n",
      "[[390  13]\n",
      " [ 24  23]]\n"
     ]
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(max_depth=2).fit(X_train, y_train)\n",
    "pred_tree = tree.predict(X_test)\n",
    "\n",
    "print(\"\\nDecision tree:\")\n",
    "print(confusion_matrix(y_test, pred_tree))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The predictions made by the decision tree make much more sense than the dummy predictions, even though the accuracy was nearly the same\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Logistic Regression\n",
      "[[402   1]\n",
      " [  7  40]]\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "logreg = LogisticRegression(C=0.01).fit(X_train, y_train) \n",
    "pred_logreg = logreg.predict(X_test) \n",
    "\n",
    "print(\"\\nLogistic Regression\") \n",
    "print(confusion_matrix(y_test, pred_logreg))\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Finally, we can see that logistic regression does better than pred_tree in all aspects: it has more true positives and true negatives while having fewer false positives and false negatives. From this comparison, it is clear that only the decision tree and the logistic regression give reasonable results, and that the logistic regression works better than the tree on all accounts. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "However, inspecting the full confusion matrix is a bit cumbersome, and while we gained a lot of insight from looking at all aspects of the matrix, the process was very manual and qualitative. There are several ways to summarize the information in the confusion matrix, which we will discuss next\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Relation to accuracy\n",
    "\\begin{equation}\n",
    "\\text{Accuracy} = \\frac{\\text{TP} + \\text{TN}}{\\text{TP} + \\text{TN} + \\text{FP} + \\text{FN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision, recall and f-score\n",
    "\\begin{equation}\n",
    "\\text{Precision} = \\frac{\\text{TP}}{\\text{TP} + \\text{FP}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> **Precision** measures how many of the samples predicted as positive are actually positive: \n",
    "> Precision is used as a performance metric when the goal is to limit the number of false positives\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Layman definition: Of all the positive predictions I made, how many of them are truly positive?"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> As an example, imagine a model for predicting whether a new drug will be effective in treating a disease in clinical trials. Clinical trials are notoriously expensive, and a pharmaceutical company will only want to run an experiment if it is very sure that the drug will actually work. Therefore, it is important that the model does not produce many false positives—in other words, that it has a high precision. Precision is also known as positive predictive value (PPV).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {
    "hide_input": false
   },
   "source": [
    "\\begin{equation}\n",
    "\\text{Recall} = \\frac{\\text{TP}}{\\text{TP} + \\text{FN}}\n",
    "\\end{equation}\n",
    "\n",
    "> Recall, on the other hand, measures how many of the positive samples are captured by the positive predictions\n",
    "\n",
    "\n",
    "\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Recall is used as performance metric when we need to identify all positive samples; that is, when it is important to avoid false negatives. The cancer diagnosis example from earlier in this chapter is a good example for this: it is important to find all people that are sick, possibly including healthy patients in the prediction. Other names for recall are sensitivity, hit rate, or true positive rate (TPR).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "If you compare the formula for precision and recall, you will notice both looks similar. The only difference is the second term of the denominator, where it is False Positive for precision but False Negative for recall"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "\n",
    "\\begin{equation}\n",
    "\\text{F} = 2 \\cdot \\frac{\\text{precision} \\cdot \\text{recall}}{\\text{precision} + \\text{recall}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> While precision and recall are very important measures, looking at only one of them will not provide you with the full picture. One way to summarize them is the f-score or f-measure, which is with the harmonic mean of precision and recall:\n",
    "\n",
    "This particular variant is also known as the f1-score. As it takes precision and recall into account, it can be a better measure than accuracy on imbalanced binary classification dataset\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "![](./images/confusion_matrix.jpg)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Accuracy and Precision:\n",
    "\n",
    "\n",
    "Accuracy refers to the closeness of a measured value to a standard or known value. For example, if in lab you obtain a weight measurement of 3.2 kg for a given substance, but the actual or known weight is 10 kg, then your measurement is not accurate. In this case, your measurement is not close to the known value.\n",
    "\n",
    "Precision refers to the closeness of two or more measurements to each other. Using the example above, if you weigh a given substance five times, and get 3.2 kg each time, then your measurement is very precise. Precision is independent of accuracy. You can be very precise but inaccurate, as described above. You can also be accurate but imprecise.\n",
    "\n",
    "For example, if on average, your measurements for a given substance are close to the known value, but the measurements are far from each other, then you have accuracy without precision.\n",
    "\n",
    "A good analogy for understanding accuracy and precision is to imagine a basketball player shooting baskets. If the player shoots with accuracy, his aim will always take the ball close to or into the basket. If the player shoots with precision, his aim will always take the ball to the same location which may or may not be close to the basket. A good player will be both accurate and precise by shooting the ball the same way each time and each time making it in the basket. "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Let’s run it on the predictions for the “nine vs. rest” dataset that we computed earlier. Here, we will assume that the “nine” class is the positive class (it is labeled as True while the rest is labeled as False), so the positive class is the minority class:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 101,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score most frequent: 0.0\n"
     ]
    }
   ],
   "source": [
    "dummy_majority = DummyClassifier(strategy='most_frequent').fit(X_train, y_train)\n",
    "pred_most_frequent = dummy_majority.predict(X_test)\n",
    "\n",
    "# f1 score\n",
    "from sklearn.metrics import f1_score\n",
    "print(f\"f1 score most frequent: {f1_score(y_test, pred_most_frequent)}\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "We can note two things here. First, we get an error message for the most_frequent prediction, as there were no predictions of the positive class (which makes the denominator in the f-score zero)\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1 score tree: 0.55\n",
      "f1 score logistic regression: 0.91\n"
     ]
    }
   ],
   "source": [
    "print(\"f1 score tree: {:.2f}\".format(f1_score(y_test, pred_tree)))\n",
    "print(\"f1 score logistic regression: {:.2f}\".format(\n",
    "    f1_score(y_test, pred_logreg)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> f-score seems to capture our intuition of what makes a good model much better than accuracy did. A disadvantage of the f-score, however, is that it is harder to interpret and explain than accuracy.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "- If we want a more comprehensive summary of precision, recall, and f1-score, we can use the classification_report convenience function to compute all three at once, and print them in a nice format\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not nine       0.90      1.00      0.94       403\n",
      "        nine       0.00      0.00      0.00        47\n",
      "\n",
      "    accuracy                           0.90       450\n",
      "   macro avg       0.45      0.50      0.47       450\n",
      "weighted avg       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import classification_report # Build a text report showing the main classification metrics.\n",
    "\n",
    "\n",
    "print(classification_report(y_test, pred_most_frequent,\n",
    "                            target_names=[\"not nine\", \"nine\"])) # TARGET NAMES: "
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> The classification_report function produces one line per class (here, True and False) and reports precision, recall, and f-score with this class as the positive class. Before, we assumed the minority “nine” class was the positive class. If we change the positive class to “not nine,” we can see from the output of classification_report that we obtain an f-score of 0.94 with the most_frequent model. Furthermore, for the “not nine” class we have a recall of 1, as we classified all samples as “not nine.” The last column next to the f-score provides the support of each class, which simply means the number of samples in this class according to the ground truth.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "More info on Macro and Micro average here: https://towardsdatascience.com/micro-macro-weighted-averages-of-f1-score-clearly-explained-b603420b292f"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not nine       0.90      1.00      0.94       403\n",
      "        nine       0.00      0.00      0.00        47\n",
      "\n",
      "    accuracy                           0.90       450\n",
      "   macro avg       0.45      0.50      0.47       450\n",
      "weighted avg       0.80      0.90      0.85       450\n",
      "\n"
     ]
    },
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n",
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/metrics/_classification.py:1318: UndefinedMetricWarning: Precision and F-score are ill-defined and being set to 0.0 in labels with no predicted samples. Use `zero_division` parameter to control this behavior.\n",
      "  _warn_prf(average, modifier, msg_start, len(result))\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_dummy,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "    not nine       0.98      1.00      0.99       403\n",
      "        nine       0.98      0.85      0.91        47\n",
      "\n",
      "    accuracy                           0.98       450\n",
      "   macro avg       0.98      0.92      0.95       450\n",
      "weighted avg       0.98      0.98      0.98       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred_logreg,\n",
    "                            target_names=[\"not nine\", \"nine\"]))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Taking uncertainty into account"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "X, y = make_blobs(n_samples=(400, 50), cluster_std=[7.0, 2],\n",
    "                  random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 1080x576 with 6 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "mglearn.plots.plot_decision_threshold()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       0.97      0.89      0.93       104\n",
      "           1       0.35      0.67      0.46         9\n",
      "\n",
      "    accuracy                           0.88       113\n",
      "   macro avg       0.66      0.78      0.70       113\n",
      "weighted avg       0.92      0.88      0.89       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, svc.predict(X_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "y_pred_lower_threshold = svc.decision_function(X_test) > -.8"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      0.82      0.90       104\n",
      "           1       0.32      1.00      0.49         9\n",
      "\n",
      "    accuracy                           0.83       113\n",
      "   macro avg       0.66      0.91      0.69       113\n",
      "weighted avg       0.95      0.83      0.87       113\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, y_pred_lower_threshold))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Precision-Recall curves and ROC curves"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.metrics import precision_recall_curve\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b5c75cf48>"
      ]
     },
     "execution_count": 60,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "# Use more data points for a smoother curve\n",
    "X, y = make_blobs(n_samples=(4000, 500), cluster_std=[7.0, 2], random_state=22)\n",
    "X_train, X_test, y_train, y_test = train_test_split(X, y, random_state=0)\n",
    "svc = SVC(gamma=.05).fit(X_train, y_train)\n",
    "precision, recall, thresholds = precision_recall_curve(\n",
    "    y_test, svc.decision_function(X_test))\n",
    "# find threshold closest to zero\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision, recall, label=\"precision recall curve\")\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b5ce47348>"
      ]
     },
     "execution_count": 61,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.ensemble import RandomForestClassifier\n",
    "\n",
    "rf = RandomForestClassifier(n_estimators=100, random_state=0, max_features=2)\n",
    "rf.fit(X_train, y_train)\n",
    "\n",
    "# RandomForestClassifier has predict_proba, but not decision_function\n",
    "precision_rf, recall_rf, thresholds_rf = precision_recall_curve(\n",
    "    y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(precision, recall, label=\"svc\")\n",
    "\n",
    "plt.plot(precision[close_zero], recall[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero svc\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.plot(precision_rf, recall_rf, label=\"rf\")\n",
    "\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(precision_rf[close_default_rf], recall_rf[close_default_rf], '^', c='k',\n",
    "         markersize=10, label=\"threshold 0.5 rf\", fillstyle=\"none\", mew=2)\n",
    "plt.xlabel(\"Precision\")\n",
    "plt.ylabel(\"Recall\")\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "f1_score of random forest: 0.610\n",
      "f1_score of svc: 0.656\n"
     ]
    }
   ],
   "source": [
    "print(\"f1_score of random forest: {:.3f}\".format(\n",
    "    f1_score(y_test, rf.predict(X_test))))\n",
    "print(\"f1_score of svc: {:.3f}\".format(f1_score(y_test, svc.predict(X_test))))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Average precision of random forest: 0.660\n",
      "Average precision of svc: 0.666\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import average_precision_score\n",
    "ap_rf = average_precision_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "ap_svc = average_precision_score(y_test, svc.decision_function(X_test))\n",
    "print(\"Average precision of random forest: {:.3f}\".format(ap_rf))\n",
    "print(\"Average precision of svc: {:.3f}\".format(ap_svc))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "##### Receiver Operating Characteristics (ROC) and AUC\n",
    "\\begin{equation}\n",
    "\\text{FPR} = \\frac{\\text{FP}}{\\text{FP} + \\text{TN}}\n",
    "\\end{equation}"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b5c9ddcc8>"
      ]
     },
     "execution_count": 64,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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EICIiUYEmAjObZmYbzWyLmd3WzPWvm9kGM3vLzJ43s8FBxiMiIk0FlgjMLAdYAFwGjATmmNnIRsXWAoXufgZQBtwfVDwiItK8IFsEE4Et7l7l7oeARUBxfAF3f8Hd/xE7fA3IDTAeERFpRpCJYCBQHXdcEzvXkhuAPzR3wcxuMrMKM6vYtWtXEkMUEZEgE4E1c86bLWj2BaAQeKC56+6+0N0L3b2wX79+SQxRRES6BnjvGiAv7jgX2N64kJldDNwJnO/unwQYj4iINCPIFsFqYJiZDTGz44DZQCS+gJmNA/4LKHL3nQHGIiIiLQgsEbh7HVAKPAu8DSx29/VmNs/MimLFHgBOAJaYWaWZRVq4nYiIBCTIriHcfTmwvNG578a9vjjIzxcRkbZpZrGISMgpEYiIhJwSgYhIyCkRiIiEXKgSgTalERFpKlSJQJvSiIg0Fejjo8cqbUoj0j61tbXU1NRw8ODBdIcibejevTu5ubl069Yt4feEMhGISPvU1NTQs2dP8vPzMWtuGTE5Frg7u3fvpqamhiFDhiT8vlB1DYlIxxw8eJA+ffooCRzjzIw+ffq0u+WmRCAiCVESyAwd+TkpEYiIhJwSgYhkhJycHMaOHUtBQQFXXHEFe/fubbi2fv16LrzwQoYPH86wYcO45557cP/n9id/+MMfKCws5PTTT+e0007jm9/8ZrOfkWi5bKNEICIZoUePHlRWVrJu3TpOPvlkFixYAMCBAwcoKiritttuY9OmTbz55pusWrWKhx9+GIB169ZRWlrKr3/9a95++23WrVvH0KFDm9w/0XItOXz4cHIqmgZ6akhE2sVsSSD3dZ+VcNlzzjmHt956C4Df/va3TJo0ialTpwJw/PHHM3/+fKZMmcLNN9/M/fffz5133slpp50GQNeuXfnKV77S5J6tlbvuuuu4/PLLmTlzJgAnnHAC+/fv58UXX+Tuu+9mwIABVFZWcsUVVzB48OCG991111307NmTb3zjGzzwwAMsXryYTz75hJKSEu6+++4O/k0ln1oEIpJRDh8+zPPPP09RUXRbk/Xr1zNhwoSjypx66qns37+fv//976xbt67J9eYkWq6xN954g/vuu48NGzYwe/ZsnnrqqYZrixcvZtasWTz33HNs3ryZN954g8rKStasWcOKFSva/VlBUYtARNqlPd/ck+nAgQOMHTuW9957jwkTJnDJJZfE4vEWn5RJxZNOEydObHhmf9y4cezcuZPt27eza9cuevfuzaBBg3jooYd47rnnGDduHAD79+9n8+bNTJ48OfD4EqEWgYhkhCNjBFu3buXQoUMNYwSjRo2ioqLiqLJVVVWccMIJ9OzZk1GjRrFmzZo2799aua5du1JfXw9EE8+hQ4carn36058+quzMmTMpKyvjqaeeYvbs2Q3vuf3226msrKSyspItW7Zwww03JF75gCkRiEhGOemkk3jooYd48MEHqa2t5dprr+Xll1/mj3/8IxBtOdxyyy1861vfAmDu3Ll8//vfZ9OmTQDU19fzox/9qMl9WyuXn5/fkCTKy8upra1tMb7Zs2ezaNEiysrKGsYULr30Uh599FH2798PwPvvv8/OncfONu1KBCKSccaNG8eYMWNYtGgRPXr0oLy8nHvvvZcRI0YwevRozjzzTEpLSwE444wz+MlPfsKcOXM4/fTTKSgo4IMPPmhyz9bK3Xjjjbz00ktMnDiR119/vUkrIN6oUaPYt28fAwcOZMCAAQBMnTqVz3/+85xzzjmMHj2amTNnsm/fvgD+ZjrG4p+1zQSFhYXeuBmYqCNPO6Srj1MkU7399tucfvrp6Q5DEtTcz8vM1rh7YXPl1SIQEQk5JQIRkZBTIhARCTnNIxCRpNu2bRuRSIS9e/fSq1cviouLycvLS3dY0gIlAhFJmh07dlBaWsqyZcsanrsHuPXWWykpKWH+/Pn076+tYo81SgQikhQ7duxg0qRJVFVV0a1bN0pKShgxYgQbN26kvLycpUuXsnbtWlatWsUpp5yS7nAljsYIRCQpSktLqaqqYvz48VRVVVFWVsZ9991HWVkZ7777bsP5m2++ud333rt3b8NqogAvvvgil19+eTLDB+Dxxx9vmH+QqPz8fD788MMm5++66y4efPDBZIUWKCUCEem0bdu2sWzZMrp160Z5eTm5ublHXc/NzeXpp5+ma9euLFu2jOrq6nbdv3EiSFQmLw3dWF1dXWD3ViIQkU6LRCLU19dTVFTUJAkckZeXR3FxMfX19UQikXbd/7bbbuOdd95h7NixzJ07F4gu3DZz5kxOO+00rr322oaNaPLz85k3bx7nnnsuS5Ys4Z133mHatGlMmDCB8847j7/85S8ALFmyhIKCAsaMGXPU4m/bt29n2rRpDBs2rGGZCoAnn3yS0aNHU1BQwLe//e1m47zvvvsYMWIEF198MRs3bmy2zNixYxv+9OjRg5deeomPP/6YL33pS5x55pmMGzeO8vJyINpCmTVrFldccQVTp07F3Zk7dy4FBQWMHj36qJVOO8XdM+rPhAkTvKNgscPiDr9fJKw2bNjQ6vV77rnHAb/jjjtaLXf77bc74Pfcc0+7Pv/dd9/1UaNGNRy/8MILfuKJJ3p1dbUfPnzYzz77bF+5cqW7uw8ePNh/+MMfNpS98MILfdOmTe7u/tprr/kFF1zg7u4FBQVeU1Pj7u579uxxd/fHHnvMhwwZ4nv37vUDBw74oEGDfNu2bf7+++97Xl6e79y502tra/2CCy7wZcuWNXzerl27vKKiwgsKCvzjjz/2jz76yE899VR/4IEHWqxTJBLxc8891w8dOuS33367P/HEEw2xDBs2zPfv3++PPfaYDxw40Hfv3u3u7mVlZX7xxRd7XV2d79ixw/Py8nz79u1N7t3czwuo8BZ+r2qwWEQ6rVevXgAtfgs+4siCbr179+70Z06cOLGh9XFkeepzzz0XgGuuuQaIthpWrVrFrFn/XFbmk08+AWDSpElcd911XH311Xzuc59ruH7RRRdx0kknATBy5Ei2bt3K7t27mTJlCv369QPg2muvZcWKFVx55ZUN71u5ciUlJSUcf/zxAA37JTRn8+bNzJ07lz/96U9069aN5557jkgk0jCmcPDgQbZt2wbAJZdcwsknnwzAyy+/zJw5c8jJyeGUU07h/PPPZ/Xq1a1+ViICTQRmNg34DyAH+Lm7/1uj658CfgVMAHYD17j7e0HEMmPGyiBuKyJEf+ndeuutRCIRampqmu0eqq6upry8nC5dunT6FxfApz71qYbXOTk5R/WhH1kUrr6+nl69elFZWdnk/Y888givv/46zzzzDGPHjm0o09x9PcE12RLZ/+Djjz/m6quv5mc/+xmf/exngWjPzNKlSxkxYsRRZRsvcJdoHO0V2BiBmeUAC4DLgJHAHDMb2ajYDcAed/9fwI+BHwYVz/LlOwCYPl3PMIsk26BBgygpKaG2tpbi4uImg8HV1dVceeWV1NXVUVJS0u7JZT179uzQap0nnngiQ4YMYcmSIwtOOm+++SYA77zzDmeddRbz5s2jb9++rQ5gn3XWWbz00kt8+OGHHD58mCeffJLzzz//qDKTJ09m2bJlHDhwgH379vG73/2u2Xtdf/31XH/99Zx33nkN5y699FJ++tOfNvyiX7t2bbPvnTx5Mk899RSHDx9m165drFixgokTJyb+F9KCIFsEE4Et7l4FYGaLgGJgQ1yZYuCu2OsyYL6ZmQeV9oBnnjmv7UIi0m7z589n7dq1/PnPf2bo0KEUFxczfPhwNm3aRHl5OXV1dQwdOrRhQ5n26NOnD5MmTaKgoIDLLruMGTNmJPze3/zmN3z5y1/m3nvvpba2ltmzZzNmzBjmzp3L5s2bcXcuuugixowZ02zLAWDAgAH84Ac/4IILLsDdmT59OsXFxUeVGT9+PNdccw1jx45l8ODBR/2iP2Lr1q2UlZWxadMmHn30UQB+/vOf853vfIevfe1rnHHGGbg7+fn5/P73v2/y/pKSEl599VXGjBmDmXH//fcnZYJeYMtQm9lMYJq7/0vs+H8DZ7l7aVyZdbEyNbHjd2JlPmx0r5uAmwAGDRo0YevWrR2IR0tQi3RUostQtzSzuEuXLpSUlLBgwQJNJkuB9i5DHWSLoLnOssZZJ5EyuPtCYCFE9yPoSDBKACLB69+/P2VlZVRXVxOJRNizZw+9e/emqKhIaw0dw4JMBDVA/E8+F9jeQpkaM+sKnAT8LcCYRCQF8vLyOjSDWNIjyAllq4FhZjbEzI4DZgONZ5FEgC/GXs8E/hTk+ICIdJz+a2aGjvycAksE7l4HlALPAm8Tncm13szmmdmRZ8d+AfQxsy3A14HbgopHRDque/fu7N69W8ngGOfu7N69m+7du7frfaHas1hEOqa2tpaamhoOHjyY7lCkDd27dyc3N5du3boddT5dg8UikiW6devGkCFD0h2GBESLzomIhJwSgYhIyCkRiIiEXMYNFpvZLqD9U4uj+gJNtxLKbqpzOKjO4dCZOg92937NXci4RNAZZlbR0qh5tlKdw0F1Doeg6qyuIRGRkFMiEBEJubAlgoXpDiANVOdwUJ3DIZA6h2qMQEREmgpbi0BERBpRIhARCbmsTARmNs3MNprZFjNrsqKpmX3KzJ6KXX/dzPJTH2VyJVDnr5vZBjN7y8yeN7PB6Ygzmdqqc1y5mWbmZpbxjxomUmczuzr2s15vZr9NdYzJlsC/7UFm9oKZrY39+56ejjiTxcweNbOdsR0cm7tuZvZQ7O/jLTMb3+kPdfes+gPkAO8AQ4HjgDeBkY3KfAV4JPZ6NvBUuuNOQZ0vAI6Pvf5yGOocK9cTWAG8BhSmO+4U/JyHAWuB3rHjz6Q77hTUeSHw5djrkcB76Y67k3WeDIwH1rVwfTrwB6I7PJ4NvN7Zz8zGFsFEYIu7V7n7IWARUNyoTDHwy9jrMuAiM2tu28xM0Wad3f0Fd/9H7PA1ojvGZbJEfs4A9wD3A9mwfnIidb4RWODuewDcfWeKY0y2ROrswImx1yfRdCfEjOLuK2h9p8Zi4Fce9RrQy8wGdOYzszERDASq445rYueaLePRDXQ+AvqkJLpgJFLneDcQ/UaRydqss5mNA/Lc/fepDCxAifychwPDzewVM3vNzKalLLpgJFLnu4AvmFkNsBz4ampCS5v2/n9vUzbuR9DcN/vGz8gmUiaTJFwfM/sCUAicH2hEwWu1zmbWBfgxcF2qAkqBRH7OXYl2D00h2upbaWYF7r434NiCkkid5wCPu/u/m9k5wBOxOtcHH15aJP33Vza2CGqAvLjjXJo2FRvKmFlXos3J1ppix7pE6oyZXQzcCRS5+ycpii0obdW5J1AAvGhm7xHtS41k+IBxov+2y9291t3fBTYSTQyZKpE63wAsBnD3V4HuRBdny1YJ/X9vj2xMBKuBYWY2xMyOIzoYHGlUJgJ8MfZ6JvAnj43CZKg26xzrJvkvokkg0/uNoY06u/tH7t7X3fPdPZ/ouEiRu2fyPqeJ/Nt+muiDAZhZX6JdRVUpjTK5EqnzNuAiADM7nWgi2JXSKFMrAvyf2NNDZwMfufsHnblh1nUNuXudmZUCzxJ94uBRd19vZvOACnePAL8g2nzcQrQlMDt9EXdegnV+ADgBWBIbF9/m7kVpC7qTEqxzVkmwzs8CU81sA3AYmOvuu9MXdeckWOdvAD8zs38l2kVyXSZ/sTOzJ4l27fWNjXt8D+gG4O6PEB0HmQ5sAf4BXN/pz8zgvy8REUmCbOwaEhGRdlAiEBEJOSUCEZGQUyIQEQk5JQIRkZBTIhBJkJkdNrPKuD/5ZjbFzD6KrXz5tpl9L1Y2/vxfzOzBdMcv0pKsm0cgEqAD7j42/kRsCfOV7n65mX0aqDSzI2sbHTnfA1hrZsvc/ZXUhizSNrUIRJLE3T8G1gCnNjp/AKikkwuDiQRFiUAkcT3iuoWWNb5oZn2Irmm0vtH53kTX+1mRmjBF2kddQyKJa9I1FHOema0F6oF/iy2BMCV2/i1gROz8jhTGKpIwJQKRzlvp7pe3dN7MhgMvx8YIKlMdnEhb1DUkEjB33wT8APh2umMRaY4SgUhqPAJMNrMh6Q5EpDGtPioiEvG/uuoAAAAuSURBVHJqEYiIhJwSgYhIyCkRiIiEnBKBiEjIKRGIiIScEoGISMgpEYiIhNz/ByAK1bIeMqdsAAAAAElFTkSuQmCC",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.metrics import roc_curve\n",
    "fpr, tpr, thresholds = roc_curve(y_test, svc.decision_function(X_test))\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve\")\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "# find threshold closest to zero\n",
    "close_zero = np.argmin(np.abs(thresholds))\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero\", fillstyle=\"none\", c='k', mew=2)\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b5caaaec8>"
      ]
     },
     "execution_count": 65,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "fpr_rf, tpr_rf, thresholds_rf = roc_curve(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "\n",
    "plt.plot(fpr, tpr, label=\"ROC Curve SVC\")\n",
    "plt.plot(fpr_rf, tpr_rf, label=\"ROC Curve RF\")\n",
    "\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR (recall)\")\n",
    "plt.plot(fpr[close_zero], tpr[close_zero], 'o', markersize=10,\n",
    "         label=\"threshold zero SVC\", fillstyle=\"none\", c='k', mew=2)\n",
    "close_default_rf = np.argmin(np.abs(thresholds_rf - 0.5))\n",
    "plt.plot(fpr_rf[close_default_rf], tpr[close_default_rf], '^', markersize=10,\n",
    "         label=\"threshold 0.5 RF\", fillstyle=\"none\", c='k', mew=2)\n",
    "\n",
    "plt.legend(loc=4)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC for Random Forest: 0.937\n",
      "AUC for SVC: 0.916\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import roc_auc_score\n",
    "rf_auc = roc_auc_score(y_test, rf.predict_proba(X_test)[:, 1])\n",
    "svc_auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "print(\"AUC for Random Forest: {:.3f}\".format(rf_auc))\n",
    "print(\"AUC for SVC: {:.3f}\".format(svc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "gamma = 1.00  accuracy = 0.90  AUC = 0.50\n",
      "gamma = 0.05  accuracy = 0.90  AUC = 1.00\n",
      "gamma = 0.01  accuracy = 0.90  AUC = 1.00\n"
     ]
    },
    {
     "data": {
      "text/plain": [
       "<matplotlib.legend.Legend at 0x20b5ce8cec8>"
      ]
     },
     "execution_count": 67,
     "metadata": {},
     "output_type": "execute_result"
    },
    {
     "data": {
      "application/pdf": 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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "y = digits.target == 9\n",
    "\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, y, random_state=0)\n",
    "\n",
    "plt.figure()\n",
    "\n",
    "for gamma in [1, 0.05, 0.01]:\n",
    "    svc = SVC(gamma=gamma).fit(X_train, y_train)\n",
    "    accuracy = svc.score(X_test, y_test)\n",
    "    auc = roc_auc_score(y_test, svc.decision_function(X_test))\n",
    "    fpr, tpr, _ = roc_curve(y_test , svc.decision_function(X_test))\n",
    "    print(\"gamma = {:.2f}  accuracy = {:.2f}  AUC = {:.2f}\".format(\n",
    "          gamma, accuracy, auc))\n",
    "    plt.plot(fpr, tpr, label=\"gamma={:.3f}\".format(gamma))\n",
    "plt.xlabel(\"FPR\")\n",
    "plt.ylabel(\"TPR\")\n",
    "plt.xlim(-0.01, 1)\n",
    "plt.ylim(0, 1.02)\n",
    "plt.legend(loc=\"best\")"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Metrics for Multiclass Classification\n",
    "\n",
    "- let’s move on to metrics to evaluate multiclass classification. Basically, all metrics for multiclass classification are derived from binary classification metrics, but averaged over all classes <br><br>\n",
    "\n",
    "- In general, multiclass classification results are harder to understand than binary classification results. <br><br>\n",
    "\n",
    "- Apart from accuracy, common tools are the confusion matrix and the classification report we saw in the binary case in the previous section\n",
    "\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Let’s apply these two detailed evaluation methods on the task of classifying the 10 different handwritten digits in the digits dataset:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 103,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0, 1, 2, ..., 8, 9, 8])"
      ]
     },
     "execution_count": 103,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "digits.target"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 102,
   "metadata": {},
   "outputs": [
    {
     "name": "stderr",
     "output_type": "stream",
     "text": [
      "/Users/shamsuddeenmuhammad/anaconda3/envs/datascience/lib/python3.9/site-packages/sklearn/linear_model/_logistic.py:814: ConvergenceWarning: lbfgs failed to converge (status=1):\n",
      "STOP: TOTAL NO. of ITERATIONS REACHED LIMIT.\n",
      "\n",
      "Increase the number of iterations (max_iter) or scale the data as shown in:\n",
      "    https://scikit-learn.org/stable/modules/preprocessing.html\n",
      "Please also refer to the documentation for alternative solver options:\n",
      "    https://scikit-learn.org/stable/modules/linear_model.html#logistic-regression\n",
      "  n_iter_i = _check_optimize_result(\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import accuracy_score\n",
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target, random_state=0)\n",
    "lr = LogisticRegression().fit(X_train, y_train)\n",
    "pred = lr.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Accuracy: 0.951\n",
      "Confusion matrix:\n",
      "[[37  0  0  0  0  0  0  0  0  0]\n",
      " [ 0 40  0  0  0  0  0  0  2  1]\n",
      " [ 0  1 40  3  0  0  0  0  0  0]\n",
      " [ 0  0  0 43  0  0  0  0  1  1]\n",
      " [ 0  0  0  0 37  0  0  1  0  0]\n",
      " [ 0  0  0  0  0 46  0  0  0  2]\n",
      " [ 0  1  0  0  0  0 51  0  0  0]\n",
      " [ 0  0  0  1  1  0  0 46  0  0]\n",
      " [ 0  3  1  0  0  0  0  0 43  1]\n",
      " [ 0  0  0  0  0  1  0  0  1 45]]\n"
     ]
    }
   ],
   "source": [
    "print(\"Accuracy: {:.3f}\".format(accuracy_score(y_test, pred)))\n",
    "print(\"Confusion matrix:\\n{}\".format(confusion_matrix(y_test, pred)))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The model has an accuracy of 95.3%, which already tells us that we are doing pretty well. The confusion matrix provides us with some more detail. As for the binary case, each row corresponds to a true label, and each column corresponds to a predicted label. You can find a visually more appealing plot\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "hide_input": false
   },
   "outputs": [
    {
     "data": {
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\n",
      "image/png": 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",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {
      "needs_background": "light"
     },
     "output_type": "display_data"
    }
   ],
   "source": [
    "scores_image = mglearn.tools.heatmap(\n",
    "    confusion_matrix(y_test, pred), xlabel='Predicted label',\n",
    "    ylabel='True label', xticklabels=digits.target_names,\n",
    "    yticklabels=digits.target_names, cmap=plt.cm.gray_r, fmt=\"%d\")\n",
    "plt.title(\"Confusion matrix\")\n",
    "plt.gca().invert_yaxis()"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> For the first class, the digit 0, there are 37 samples in the class, and all of these samples were classified as class 0 (there are no false negatives for class 0). We can see that because all other entries in the first row of the confusion matrix are 0. We can also see that no other digits were mistakenly classified as 0, because all other entries in the first column of the confusion matrix are 0 (there are no false positives for class 0). Some digits were confused with others, though—for example, the digit 2 (third row), three of which were classified as the digit 3 (fourth column). There was also one digit 3 that was classified as 2 (third column, fourth row) and one digit 8 that was classified as 2 (thrid column, fourth row).\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Also, with the classification_report function, we can compute the precision, recall, and f-score for each class:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "              precision    recall  f1-score   support\n",
      "\n",
      "           0       1.00      1.00      1.00        37\n",
      "           1       0.89      0.93      0.91        43\n",
      "           2       0.98      0.91      0.94        44\n",
      "           3       0.91      0.96      0.93        45\n",
      "           4       0.97      0.97      0.97        38\n",
      "           5       0.98      0.96      0.97        48\n",
      "           6       1.00      0.98      0.99        52\n",
      "           7       0.98      0.96      0.97        48\n",
      "           8       0.91      0.90      0.91        48\n",
      "           9       0.90      0.96      0.93        47\n",
      "\n",
      "    accuracy                           0.95       450\n",
      "   macro avg       0.95      0.95      0.95       450\n",
      "weighted avg       0.95      0.95      0.95       450\n",
      "\n"
     ]
    }
   ],
   "source": [
    "print(classification_report(y_test, pred))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> Unsurprisingly, precision and recall are a perfect 1 for class 0, as there are no confusions with this class. For class 7, on the other hand, precision is 1 because no other class was mistakenly classified as 7, while for class 6, there are no false negatives, so the recall is 1. We can also see that the model has particular difficulties with classes 8 and 3\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "The most commonly used metric for imbalanced datasets in the multiclass setting is the multiclass version of the f-score. The idea behind the multiclass f-score is to compute one binary f-score per class, with that class being the positive class and the other classes making up the negative classes. Then, these per-class f-scores are averaged using one of the following strategies\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \"macro\" averaging computes the unweighted per-class f-scores. This gives equal weight to all classes, no matter what their size is. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \"weighted\" averaging computes the mean of the per-class f-scores, weighted by their support. This is what is reported in the classification report. \n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> \"micro\" averaging computes the total number of false positives, false negatives, and true positives over all classes, and then computes precision, recall, and fscore using these counts."
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "> If you care about each sample equally much, it is recommended to use the \"micro\" average f1-score; if you care about each class equally much, it is recommended to use the \"macro\" average f1-score\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Micro average f1 score: 0.951\n",
      "Macro average f1 score: 0.952\n",
      "Weighted average f1 score: 0.951\n"
     ]
    }
   ],
   "source": [
    "print(\"Micro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"micro\")))\n",
    "\n",
    "print(\"Macro average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"macro\")))\n",
    "\n",
    "\n",
    "print(\"Weighted average f1 score: {:.3f}\".format(\n",
    "    f1_score(y_test, pred, average=\"weighted\")))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "#### Regression metrics\n",
    "\n",
    "- Evaluation for regression can be done in similar detail as we did for classificationfor example, but using the default R2 used in the score method of all regressors is enough. <br><br>\n",
    "- Sometimes business decisions are made on the basis of mean squared error or mean absolute error, which might give incentive to tune models using these metrics.<br><br>\n",
    "- In general, though, we have found R2 to be a more intuitive metric to evaluate regression models.\n"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Using evaluation metrics in model selection\n",
    "\n",
    "- We have discussed many evaluation methods in detail. <br><br>\n",
    "- However, we often want to use metrics like AUC in model selection using GridSearchCV or cross_val_score. <br><br>\n",
    "- Luckily scikit-learn provides a very simple way to achieve this, via the scoring argument that can be used in both GridSearchCV and cross_val_score. <br><br>\n",
    "- You can simply provide a string describing the evaluation metric you want to use. "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 81,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Default scoring: [0.975      0.99166667 1.         0.99442897 0.98050139]\n",
      "Explicit accuracy scoring: [0.975      0.99166667 1.         0.99442897 0.98050139]\n"
     ]
    }
   ],
   "source": [
    "from sklearn.svm import SVC\n",
    "from sklearn.model_selection import cross_val_score\n",
    "\n",
    "# default scoring for classification is accuracy\n",
    "print(\"Default scoring: {}\".format(\n",
    "    cross_val_score(SVC(), digits.data, digits.target == 9, cv=5)))\n",
    "# providing scoring=\"accuracy\" doesn't change the results\n",
    "explicit_accuracy =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                                     scoring=\"accuracy\", cv=5)\n",
    "print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 82,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "AUC scoring: [0.99717078 0.99854252 1.         0.999828   0.98400413]\n"
     ]
    }
   ],
   "source": [
    "roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "                           scoring=\"roc_auc\", cv=5)\n",
    "print(\"AUC scoring: {}\".format(roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "\n",
    "# print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n",
    "# roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "#                            scoring=\"roc_auc\", cv=5)\n",
    "# print(\"AUC scoring: {}\".format(roc_auc))\n",
    "# print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n",
    "\n",
    "# roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "#                            scoring=\"roc_auc\", cv=5)\n",
    "# print(\"AUC scoring: {}\".format(roc_auc))\n",
    "# print(\"Explicit accuracy scoring: {}\".format(explicit_accuracy))\n",
    "\n",
    "# roc_auc =  cross_val_score(SVC(), digits.data, digits.target == 9,\n",
    "#                            scoring=\"roc_auc\", cv=5)\n",
    "# print(\"AUC scoring: {}\".format(roc_auc))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 87,
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<div>\n",
       "<style scoped>\n",
       "    .dataframe tbody tr th:only-of-type {\n",
       "        vertical-align: middle;\n",
       "    }\n",
       "\n",
       "    .dataframe tbody tr th {\n",
       "        vertical-align: top;\n",
       "    }\n",
       "\n",
       "    .dataframe thead th {\n",
       "        text-align: right;\n",
       "    }\n",
       "</style>\n",
       "<table border=\"1\" class=\"dataframe\">\n",
       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>fit_time</th>\n",
       "      <th>score_time</th>\n",
       "      <th>test_accuracy</th>\n",
       "      <th>train_accuracy</th>\n",
       "      <th>test_roc_auc</th>\n",
       "      <th>train_roc_auc</th>\n",
       "      <th>test_recall_macro</th>\n",
       "      <th>train_recall_macro</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>0.041086</td>\n",
       "      <td>0.116764</td>\n",
       "      <td>0.975000</td>\n",
       "      <td>0.994433</td>\n",
       "      <td>0.997171</td>\n",
       "      <td>0.998679</td>\n",
       "      <td>0.887346</td>\n",
       "      <td>0.972222</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>0.119948</td>\n",
       "      <td>0.240315</td>\n",
       "      <td>0.991667</td>\n",
       "      <td>0.995825</td>\n",
       "      <td>0.998543</td>\n",
       "      <td>0.999141</td>\n",
       "      <td>0.958333</td>\n",
       "      <td>0.982252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>0.297609</td>\n",
       "      <td>0.227275</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.995132</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.998975</td>\n",
       "      <td>1.000000</td>\n",
       "      <td>0.978780</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>0.124612</td>\n",
       "      <td>0.166793</td>\n",
       "      <td>0.994429</td>\n",
       "      <td>0.995828</td>\n",
       "      <td>0.999828</td>\n",
       "      <td>0.998825</td>\n",
       "      <td>0.972222</td>\n",
       "      <td>0.982252</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>0.201213</td>\n",
       "      <td>0.190917</td>\n",
       "      <td>0.980501</td>\n",
       "      <td>0.996523</td>\n",
       "      <td>0.984004</td>\n",
       "      <td>0.999914</td>\n",
       "      <td>0.902778</td>\n",
       "      <td>0.985725</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n",
       "</div>"
      ],
      "text/plain": [
       "   fit_time  score_time  test_accuracy  train_accuracy  test_roc_auc  \\\n",
       "0  0.041086    0.116764       0.975000        0.994433      0.997171   \n",
       "1  0.119948    0.240315       0.991667        0.995825      0.998543   \n",
       "2  0.297609    0.227275       1.000000        0.995132      1.000000   \n",
       "3  0.124612    0.166793       0.994429        0.995828      0.999828   \n",
       "4  0.201213    0.190917       0.980501        0.996523      0.984004   \n",
       "\n",
       "   train_roc_auc  test_recall_macro  train_recall_macro  \n",
       "0       0.998679           0.887346            0.972222  \n",
       "1       0.999141           0.958333            0.982252  \n",
       "2       0.998975           1.000000            0.978780  \n",
       "3       0.998825           0.972222            0.982252  \n",
       "4       0.999914           0.902778            0.985725  "
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "from sklearn.model_selection import cross_validate\n",
    "\n",
    "res = cross_validate(SVC(), digits.data, digits.target == 9,\n",
    "                     scoring=[\"accuracy\", \"roc_auc\", \"recall_macro\"],\n",
    "                     return_train_score=True, cv=5)\n",
    "display(pd.DataFrame(res))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "Similarly, we can change the metric used to pick the best parameters in Grid SearchCV:\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Grid-Search with accuracy\n",
      "Best parameters: {'gamma': 0.0001}\n",
      "Best cross-validation score (accuracy)): 0.976\n",
      "Test set AUC: 0.992\n",
      "Test set accuracy: 0.973\n"
     ]
    }
   ],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(\n",
    "    digits.data, digits.target == 9, random_state=0)\n",
    "\n",
    "# we provide a somewhat bad grid to illustrate the point:\n",
    "param_grid = {'gamma': [0.0001, 0.01, 0.1, 1, 10]}\n",
    "# using the default scoring of accuracy:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid)\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"Grid-Search with accuracy\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (accuracy)): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "\n",
      "Grid-Search with AUC\n",
      "Best parameters: {'gamma': 0.01}\n",
      "Best cross-validation score (AUC): 0.998\n",
      "Test set AUC: 1.000\n",
      "Test set accuracy: 1.000\n"
     ]
    }
   ],
   "source": [
    "# using AUC scoring instead:\n",
    "grid = GridSearchCV(SVC(), param_grid=param_grid, scoring=\"roc_auc\")\n",
    "grid.fit(X_train, y_train)\n",
    "print(\"\\nGrid-Search with AUC\")\n",
    "print(\"Best parameters:\", grid.best_params_)\n",
    "print(\"Best cross-validation score (AUC): {:.3f}\".format(grid.best_score_))\n",
    "print(\"Test set AUC: {:.3f}\".format(\n",
    "    roc_auc_score(y_test, grid.decision_function(X_test))))\n",
    "print(\"Test set accuracy: {:.3f}\".format(grid.score(X_test, y_test)))"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Available scorers:\n",
      "['accuracy', 'adjusted_mutual_info_score', 'adjusted_rand_score', 'average_precision', 'balanced_accuracy', 'completeness_score', 'explained_variance', 'f1', 'f1_macro', 'f1_micro', 'f1_samples', 'f1_weighted', 'fowlkes_mallows_score', 'homogeneity_score', 'jaccard', 'jaccard_macro', 'jaccard_micro', 'jaccard_samples', 'jaccard_weighted', 'max_error', 'mutual_info_score', 'neg_brier_score', 'neg_log_loss', 'neg_mean_absolute_error', 'neg_mean_gamma_deviance', 'neg_mean_poisson_deviance', 'neg_mean_squared_error', 'neg_mean_squared_log_error', 'neg_median_absolute_error', 'neg_root_mean_squared_error', 'normalized_mutual_info_score', 'precision', 'precision_macro', 'precision_micro', 'precision_samples', 'precision_weighted', 'r2', 'recall', 'recall_macro', 'recall_micro', 'recall_samples', 'recall_weighted', 'roc_auc', 'roc_auc_ovo', 'roc_auc_ovo_weighted', 'roc_auc_ovr', 'roc_auc_ovr_weighted', 'v_measure_score']\n"
     ]
    }
   ],
   "source": [
    "from sklearn.metrics import SCORERS\n",
    "print(\"Available scorers:\")\n",
    "print(sorted(SCORERS.keys()))"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### Summary and Outlook"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
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