Logistic Regression and Confusion Matrix/Logistic regression.ipynb

{
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  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "<h2>Logistic Regression: Multiclass Classification</h2>"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_digits\n",
    "%matplotlib inline\n",
    "import matplotlib.pyplot as plt\n",
    "digits = load_digits()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "metadata": {},
   "outputs": [
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     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "plt.gray() \n",
    "for i in range(5):\n",
    "    plt.matshow(digits.images[i]) "
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "['DESCR', 'data', 'images', 'target', 'target_names']"
     },
     "metadata": {},
     "execution_count": 3
    }
   ],
   "source": [
    "dir(digits)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([ 0.,  0.,  5., 13.,  9.,  1.,  0.,  0.,  0.,  0., 13., 15., 10.,\n       15.,  5.,  0.,  0.,  3., 15.,  2.,  0., 11.,  8.,  0.,  0.,  4.,\n       12.,  0.,  0.,  8.,  8.,  0.,  0.,  5.,  8.,  0.,  0.,  9.,  8.,\n        0.,  0.,  4., 11.,  0.,  1., 12.,  7.,  0.,  0.,  2., 14.,  5.,\n       10., 12.,  0.,  0.,  0.,  0.,  6., 13., 10.,  0.,  0.,  0.])"
     },
     "metadata": {},
     "execution_count": 4
    }
   ],
   "source": [
    "digits.data[0]"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Model"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "from sklearn.linear_model import LogisticRegression\n",
    "model = LogisticRegression()\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split(digits.data,digits.target, test_size=0.2)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "LogisticRegression(C=1.0, class_weight=None, dual=False, fit_intercept=True,\n                   intercept_scaling=1, l1_ratio=None, max_iter=100,\n                   multi_class='auto', n_jobs=None, penalty='l2',\n                   random_state=None, solver='lbfgs', tol=0.0001, verbose=0,\n                   warm_start=False)"
     },
     "metadata": {},
     "execution_count": 8
    }
   ],
   "source": [
    "model.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "### model accuracy"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "0.9638888888888889"
     },
     "metadata": {},
     "execution_count": 9
    }
   ],
   "source": [
    "model.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([0, 1, 2, 3, 4])"
     },
     "metadata": {},
     "execution_count": 10
    }
   ],
   "source": [
    "model.predict(digits.data[0:5])"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Plotting Confusion matrix"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "metadata": {
    "collapsed": true
   },
   "outputs": [],
   "source": [
    "y_predicted = model.predict(X_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "array([[36,  0,  0,  0,  0,  0,  0,  0,  0,  0],\n       [ 0, 27,  0,  0,  1,  0,  0,  0,  0,  0],\n       [ 0,  0, 31,  0,  0,  0,  0,  0,  0,  0],\n       [ 0,  0,  1, 42,  0,  0,  0,  0,  0,  0],\n       [ 0,  1,  0,  0, 39,  0,  0,  0,  1,  0],\n       [ 0,  0,  0,  0,  1, 30,  0,  0,  0,  2],\n       [ 1,  0,  0,  0,  0,  0, 39,  0,  0,  0],\n       [ 0,  0,  0,  0,  0,  0,  0, 38,  0,  1],\n       [ 0,  2,  0,  0,  0,  0,  0,  0, 25,  1],\n       [ 0,  0,  0,  0,  0,  0,  0,  0,  1, 40]], dtype=int64)"
     },
     "metadata": {},
     "execution_count": 12
    }
   ],
   "source": [
    "from sklearn.metrics import confusion_matrix\n",
    "cm = confusion_matrix(y_test, y_predicted)\n",
    "cm"
   ]
  },
  {
   "cell_type": "markdown",
   "metadata": {},
   "source": [
    "## Using seaborn that is more visually appealing"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "metadata": {},
   "outputs": [
    {
     "output_type": "execute_result",
     "data": {
      "text/plain": "Text(69.0, 0.5, 'Truth')"
     },
     "metadata": {},
     "execution_count": 13
    },
    {
     "output_type": "display_data",
     "data": {
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\n"
     },
     "metadata": {
      "needs_background": "light"
     }
    }
   ],
   "source": [
    "import seaborn as sn\n",
    "plt.figure(figsize = (10,7))\n",
    "sn.heatmap(cm, annot=True)\n",
    "plt.xlabel('Predicted')\n",
    "plt.ylabel('Truth')"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.8.2-final"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 2
}