Lab 14- Decision Tree and RF Demo.ipynb

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  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "941e1f64",
   "metadata": {},
   "outputs": [],
   "source": [
    "from sklearn.datasets import load_iris, load_breast_cancer\n",
    "from sklearn.model_selection import train_test_split\n",
    "\n",
    "from sklearn.tree import DecisionTreeClassifier\n",
    "from sklearn.ensemble import RandomForestClassifier"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "b8d2519e",
   "metadata": {},
   "outputs": [],
   "source": [
    "iris_dataset = load_iris()\n",
    "cancer_dataset = load_breast_cancer()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "242ac953",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['mean radius', 'mean texture', 'mean perimeter', 'mean area',\n",
       "       'mean smoothness', 'mean compactness', 'mean concavity',\n",
       "       'mean concave points', 'mean symmetry', 'mean fractal dimension',\n",
       "       'radius error', 'texture error', 'perimeter error', 'area error',\n",
       "       'smoothness error', 'compactness error', 'concavity error',\n",
       "       'concave points error', 'symmetry error',\n",
       "       'fractal dimension error', 'worst radius', 'worst texture',\n",
       "       'worst perimeter', 'worst area', 'worst smoothness',\n",
       "       'worst compactness', 'worst concavity', 'worst concave points',\n",
       "       'worst symmetry', 'worst fractal dimension'], dtype='<U23')"
      ]
     },
     "execution_count": 4,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.feature_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "42ebd234",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array(['malignant', 'benign'], dtype='<U9')"
      ]
     },
     "execution_count": 5,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "cancer_dataset.target_names"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "e6f813e3",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(iris_dataset.data, iris_dataset.target, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "df95300b",
   "metadata": {},
   "outputs": [],
   "source": [
    "clf = DecisionTreeClassifier()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "55ccf284",
   "metadata": {},
   "outputs": [
    {
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      ],
      "text/plain": [
       "DecisionTreeClassifier()"
      ]
     },
     "execution_count": 8,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "clf.fit(X_train, y_train)"
   ]
  },
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   "execution_count": 9,
   "id": "254a28a0",
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       "1.0"
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     "execution_count": 9,
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   "source": [
    "clf.score(X_train, y_train)"
   ]
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   "execution_count": 10,
   "id": "0c518671",
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       "0.9736842105263158"
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    "clf.score(X_test, y_test)"
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  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "332a2002",
   "metadata": {},
   "outputs": [],
   "source": [
    "# you need to install graphvis utility and graphviz in anaconda using command: pip install graphviz\n",
    "from sklearn.tree import export_graphviz\n",
    "import graphviz"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "2d6b040a",
   "metadata": {},
   "outputs": [],
   "source": [
    "dot_data = export_graphviz(clf, out_file=None, feature_names=iris_dataset.feature_names,\n",
    "                          class_names=iris_dataset.target_names, filled=True, rounded=True)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "899fe130",
   "metadata": {},
   "outputs": [
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       "<title>6</title>\n",
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       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
       "<text text-anchor=\"middle\" x=\"98.5\" y=\"-22.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">value = [0, 0, 3]</text>\n",
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       "</g>\n",
       "<!-- 5&#45;&gt;6 -->\n",
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       "<title>5&#45;&gt;6</title>\n",
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       "<text text-anchor=\"middle\" x=\"238.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<!-- 5&#45;&gt;7 -->\n",
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       "<title>9</title>\n",
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       "<text text-anchor=\"middle\" x=\"413.5\" y=\"-141.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 4</text>\n",
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       "<title>8&#45;&gt;9</title>\n",
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       "<text text-anchor=\"middle\" x=\"577.5\" y=\"-164.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"577.5\" y=\"-149.3\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 35</text>\n",
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       "<title>8&#45;&gt;12</title>\n",
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       "<!-- 10 -->\n",
       "<g id=\"node11\" class=\"node\">\n",
       "<title>10</title>\n",
       "<path fill=\"#8139e5\" stroke=\"black\" d=\"M448.5,-68C448.5,-68 356.5,-68 356.5,-68 350.5,-68 344.5,-62 344.5,-56 344.5,-56 344.5,-12 344.5,-12 344.5,-6 350.5,0 356.5,0 356.5,0 448.5,0 448.5,0 454.5,0 460.5,-6 460.5,-12 460.5,-12 460.5,-56 460.5,-56 460.5,-62 454.5,-68 448.5,-68\"/>\n",
       "<text text-anchor=\"middle\" x=\"402.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"402.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 3</text>\n",
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       "<title>11</title>\n",
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       "<text text-anchor=\"middle\" x=\"542.5\" y=\"-52.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">gini = 0.0</text>\n",
       "<text text-anchor=\"middle\" x=\"542.5\" y=\"-37.8\" font-family=\"Helvetica,sans-Serif\" font-size=\"14.00\">samples = 1</text>\n",
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       "<title>9&#45;&gt;11</title>\n",
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       "</g>\n",
       "</g>\n",
       "</svg>\n"
      ],
      "text/plain": [
       "<graphviz.sources.Source at 0x1c384494d30>"
      ]
     },
     "execution_count": 13,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "graphviz.Source(dot_data)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "ff86e155",
   "metadata": {},
   "source": [
    "### Breast Cancer Dataset"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "887a2aec",
   "metadata": {},
   "outputs": [],
   "source": [
    "X_train, X_test, y_train, y_test = train_test_split(cancer_dataset.data, cancer_dataset.target, random_state=42)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "35c6bc25",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {color: black;background-color: white;}#sk-container-id-2 pre{padding: 0;}#sk-container-id-2 div.sk-toggleable {background-color: white;}#sk-container-id-2 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-2 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-2 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-2 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-2 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-2 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-2 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-2 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-2 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-2 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-2 div.sk-item {position: relative;z-index: 1;}#sk-container-id-2 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-2 div.sk-item::before, #sk-container-id-2 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-2 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-2 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-2 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-2 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-2 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-2 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-2 div.sk-label-container {text-align: center;}#sk-container-id-2 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-2 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" checked><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(random_state=0)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=0)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "57b1b621",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 16,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "fe716ad1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9300699300699301"
      ]
     },
     "execution_count": 17,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "23ba8356",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-3 {color: black;background-color: white;}#sk-container-id-3 pre{padding: 0;}#sk-container-id-3 div.sk-toggleable {background-color: white;}#sk-container-id-3 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-3 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-3 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-3 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-3 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-3 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-3 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-3 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-3 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-3 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-3 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-3 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-3 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-3 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-3 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-3 div.sk-item {position: relative;z-index: 1;}#sk-container-id-3 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-3 div.sk-item::before, #sk-container-id-3 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-3 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-3 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-3 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-3 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-3 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-3 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-3 div.sk-label-container {text-align: center;}#sk-container-id-3 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-3 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-3\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeClassifier(max_depth=4, random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" checked><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeClassifier</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeClassifier(max_depth=4, random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "DecisionTreeClassifier(max_depth=4, random_state=0)"
      ]
     },
     "execution_count": 18,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree = DecisionTreeClassifier(random_state=0, max_depth=4)\n",
    "tree.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "50950d2e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.9953051643192489"
      ]
     },
     "execution_count": 19,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "eafb4941",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.951048951048951"
      ]
     },
     "execution_count": 20,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "e4bb5875",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "array([0.        , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.73943775, 0.        , 0.        ,\n",
       "       0.013032  , 0.        , 0.        , 0.        , 0.        ,\n",
       "       0.        , 0.        , 0.01737208, 0.00684355, 0.        ,\n",
       "       0.06019401, 0.11783988, 0.        , 0.03522339, 0.01005736,\n",
       "       0.        , 0.        , 0.        , 0.        , 0.        ])"
      ]
     },
     "execution_count": 21,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "tree.feature_importances_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 24,
   "id": "55b3dddf",
   "metadata": {},
   "outputs": [],
   "source": [
    "import matplotlib.pyplot as plt\n",
    "import numpy as np\n",
    "\n",
    "def plot_feature_importances_cancer(model):\n",
    "    n_features = cancer_dataset.data.shape[1]\n",
    "    plt.barh(range(n_features), model.feature_importances_, align='center')\n",
    "    plt.yticks(np.arange(n_features), cancer_dataset.feature_names)\n",
    "    \n",
    "    plt.xlabel(\"Feature Importance\")\n",
    "    plt.ylabel(\"Feature\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 25,
   "id": "add46ba9",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_cancer(tree)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 26,
   "id": "0a1576f4",
   "metadata": {},
   "outputs": [],
   "source": [
    "forest = RandomForestClassifier(n_estimators=100, random_state=0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 27,
   "id": "17dca887",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-4 {color: black;background-color: white;}#sk-container-id-4 pre{padding: 0;}#sk-container-id-4 div.sk-toggleable {background-color: white;}#sk-container-id-4 label.sk-toggleable__label {cursor: pointer;display: block;width: 100%;margin-bottom: 0;padding: 0.3em;box-sizing: border-box;text-align: center;}#sk-container-id-4 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-4 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-4 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-4 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-4 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-4 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-4 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-4 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 input.sk-hidden--visually {border: 0;clip: rect(1px 1px 1px 1px);clip: rect(1px, 1px, 1px, 1px);height: 1px;margin: -1px;overflow: hidden;padding: 0;position: absolute;width: 1px;}#sk-container-id-4 div.sk-estimator {font-family: monospace;background-color: #f0f8ff;border: 1px dotted black;border-radius: 0.25em;box-sizing: border-box;margin-bottom: 0.5em;}#sk-container-id-4 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-4 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-4 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-4 div.sk-serial::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: 0;}#sk-container-id-4 div.sk-serial {display: flex;flex-direction: column;align-items: center;background-color: white;padding-right: 0.2em;padding-left: 0.2em;position: relative;}#sk-container-id-4 div.sk-item {position: relative;z-index: 1;}#sk-container-id-4 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-4 div.sk-item::before, #sk-container-id-4 div.sk-parallel-item::before {content: \"\";position: absolute;border-left: 1px solid gray;box-sizing: border-box;top: 0;bottom: 0;left: 50%;z-index: -1;}#sk-container-id-4 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-4 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-4 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-4 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-4 div.sk-dashed-wrapped {border: 1px dashed gray;margin: 0 0.4em 0.5em 0.4em;box-sizing: border-box;padding-bottom: 0.4em;background-color: white;}#sk-container-id-4 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-4 div.sk-label-container {text-align: center;}#sk-container-id-4 div.sk-container {/* jupyter's `normalize.less` sets `[hidden] { display: none; }` but bootstrap.min.css set `[hidden] { display: none !important; }` so we also need the `!important` here to be able to override the default hidden behavior on the sphinx rendered scikit-learn.org. See: https://github.com/scikit-learn/scikit-learn/issues/21755 */display: inline-block !important;position: relative;}#sk-container-id-4 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-4\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>RandomForestClassifier(random_state=0)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">RandomForestClassifier</label><div class=\"sk-toggleable__content\"><pre>RandomForestClassifier(random_state=0)</pre></div></div></div></div></div>"
      ],
      "text/plain": [
       "RandomForestClassifier(random_state=0)"
      ]
     },
     "execution_count": 27,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.fit(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 28,
   "id": "25610aa3",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "1.0"
      ]
     },
     "execution_count": 28,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(X_train, y_train)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 30,
   "id": "169e310b",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "0.972027972027972"
      ]
     },
     "execution_count": 30,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "forest.score(X_test, y_test)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 31,
   "id": "45bcfc56",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 640x480 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plot_feature_importances_cancer(forest)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "ec4011ba",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "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": "3.10.9"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}