DecisionTree_reductionFeatures_test.ipynb

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   "source": [
    "from sklearn.metrics import mean_absolute_error, mean_squared_error\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import time\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "from sklearn.tree import export_graphviz\n",
    "import pydot\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
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   "source": [
    "dataset_train_features = pd.read_csv('facebook_general_dataset/Dataset/Training/Features_All.csv', header = None)\n",
    "test_train_features = pd.read_csv('facebook_general_dataset/Dataset/Testing/Features_TestSet.csv', header=None)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
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   "source": [
    "X_train_features = dataset_train_features.drop([0,1,2,3,4,9,14,19,24,26,27,31,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52, 53], axis = 1)\n",
    "y_train = dataset_train_features.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "metadata": {
    "collapsed": true
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   "outputs": [],
   "source": [
    "X_test = test_train_features.drop([0,1,2,3,4,9,14,19,24,26,27,31,34,35,36,37,38,39,40,41,42,43,44,45,46,47,48,49,50,51,52, 53], axis = 1)\n",
    "y_test = test_train_features.iloc[:,-1]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
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   "source": [
    "clf = DecisionTreeRegressor(criterion ='mse',\n",
    "                                 max_features = None,\n",
    "                                 max_depth = None,\n",
    "                                 min_samples_leaf = 1,\n",
    "                                 min_samples_split = 2)\n",
    "clf = clf.fit(X_train_features, y_train)\n"
   ]
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  {
   "cell_type": "code",
   "execution_count": 25,
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   "outputs": [],
   "source": [
    "arquivo = \"/home/gesiel/Documentos/machine learning/tree.dot\""
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {},
   "outputs": [],
   "source": [
    "export_graphviz(clf, out_file = arquivo)\n",
    "with open(arquivo) as f:\n",
    "    dot_graph = f.read()\n",
    "graphviz.Source(dot_graph)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
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   "outputs": [],
   "source": [
    "!dot -Tpng tree.dot -o tree.png"
   ]
  },
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   "cell_type": "code",
   "execution_count": 22,
   "metadata": {
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   "source": [
    "y_pred = clf.predict(X_test)"
   ]
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   "execution_count": 23,
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     "execution_count": 23,
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   "source": [
    "mean_absolute_error(y_test, y_pred)"
   ]
  },
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   "cell_type": "code",
   "execution_count": 24,
   "metadata": {},
   "outputs": [
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     "execution_count": 24,
     "metadata": {},
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   ],
   "source": [
    "mean_squared_error(y_test, y_pred)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "metadata": {
    "collapsed": true
   },
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