F1 Scores.ipynb

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    "# As usual, use a train/test split to get a reliable F1 score from two classifiers, and\n",
    "# save it the scores in the provided dictionaries.\n",
    "\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.metrics import f1_score\n",
    "from sklearn.naive_bayes import GaussianNB\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "# Load the dataset\n",
    "X = pd.read_csv('titanic_data.csv')\n",
    "\n",
    "X = X._get_numeric_data()\n",
    "y = X['Survived']\n",
    "del X['Age'], X['Survived']"
   ]
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    "# TODO: split the data into training and testing sets,\n",
    "# using the standard settings for train_test_split.\n",
    "# Then, train and test the classifiers with your newly split data instead of X and y.\n",
    "\n",
    "xTrain, xTest, yTrain, yTest = train_test_split(X, y, test_size=0.4, random_state=0)"
   ]
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     "text": [
      "Decision Tree F1 score: 0.52\n",
      "GaussianNB F1 score: 0.47\n"
     ]
    }
   ],
   "source": [
    "clf1 = DecisionTreeClassifier()\n",
    "clf1.fit(xTrain, yTrain)\n",
    "dtaTreeOne = f1_score(yTest, clf1.predict(xTest))\n",
    "print \"Decision Tree F1 score: {:.2f}\".format(dtaTreeOne)\n",
    "\n",
    "clf2 = GaussianNB()\n",
    "clf2.fit(xTrain, yTrain)\n",
    "nbOne = f1_score(yTest, clf2.predict(xTest))\n",
    "print \"GaussianNB F1 score: {:.2f}\".format(nbOne)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "metadata": {
    "collapsed": true
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    "F1_scores = {\n",
    " \"Naive Bayes\": dtaTreeOne,\n",
    " \"Decision Tree\": nbOne\n",
    "}"
   ]
  },
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