random-forest.ipynb

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       "  <thead>\n",
       "    <tr style=\"text-align: right;\">\n",
       "      <th></th>\n",
       "      <th>id</th>\n",
       "      <th>x1</th>\n",
       "      <th>x2</th>\n",
       "      <th>x3</th>\n",
       "      <th>x4</th>\n",
       "      <th>y</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th>0</th>\n",
       "      <td>1</td>\n",
       "      <td>6.80</td>\n",
       "      <td>2.4</td>\n",
       "      <td>8.81</td>\n",
       "      <td>2.82</td>\n",
       "      <td>3.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>1</th>\n",
       "      <td>2</td>\n",
       "      <td>8.10</td>\n",
       "      <td>1.8</td>\n",
       "      <td>4.58</td>\n",
       "      <td>12.40</td>\n",
       "      <td>11.6</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>2</th>\n",
       "      <td>3</td>\n",
       "      <td>3.11</td>\n",
       "      <td>9.3</td>\n",
       "      <td>5.88</td>\n",
       "      <td>12.23</td>\n",
       "      <td>5.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>3</th>\n",
       "      <td>4</td>\n",
       "      <td>2.26</td>\n",
       "      <td>1.6</td>\n",
       "      <td>5.91</td>\n",
       "      <td>3.81</td>\n",
       "      <td>1.9</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>4</th>\n",
       "      <td>5</td>\n",
       "      <td>6.03</td>\n",
       "      <td>4.2</td>\n",
       "      <td>6.84</td>\n",
       "      <td>10.75</td>\n",
       "      <td>4.0</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>5</th>\n",
       "      <td>6</td>\n",
       "      <td>9.69</td>\n",
       "      <td>5.2</td>\n",
       "      <td>1.87</td>\n",
       "      <td>2.63</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>6</th>\n",
       "      <td>7</td>\n",
       "      <td>8.59</td>\n",
       "      <td>6.7</td>\n",
       "      <td>1.26</td>\n",
       "      <td>11.99</td>\n",
       "      <td>7.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>7</th>\n",
       "      <td>8</td>\n",
       "      <td>2.47</td>\n",
       "      <td>2.2</td>\n",
       "      <td>5.17</td>\n",
       "      <td>10.67</td>\n",
       "      <td>9.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>8</th>\n",
       "      <td>9</td>\n",
       "      <td>9.38</td>\n",
       "      <td>4.4</td>\n",
       "      <td>6.41</td>\n",
       "      <td>9.54</td>\n",
       "      <td>3.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>9</th>\n",
       "      <td>10</td>\n",
       "      <td>4.79</td>\n",
       "      <td>8.2</td>\n",
       "      <td>5.14</td>\n",
       "      <td>13.10</td>\n",
       "      <td>4.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>10</th>\n",
       "      <td>11</td>\n",
       "      <td>3.57</td>\n",
       "      <td>7.5</td>\n",
       "      <td>9.15</td>\n",
       "      <td>14.92</td>\n",
       "      <td>9.7</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>11</th>\n",
       "      <td>12</td>\n",
       "      <td>3.01</td>\n",
       "      <td>2.9</td>\n",
       "      <td>7.84</td>\n",
       "      <td>14.81</td>\n",
       "      <td>1.5</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>12</th>\n",
       "      <td>13</td>\n",
       "      <td>4.11</td>\n",
       "      <td>2.9</td>\n",
       "      <td>8.99</td>\n",
       "      <td>6.27</td>\n",
       "      <td>11.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>13</th>\n",
       "      <td>14</td>\n",
       "      <td>4.75</td>\n",
       "      <td>3.4</td>\n",
       "      <td>5.89</td>\n",
       "      <td>3.39</td>\n",
       "      <td>11.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>14</th>\n",
       "      <td>15</td>\n",
       "      <td>2.59</td>\n",
       "      <td>9.7</td>\n",
       "      <td>7.95</td>\n",
       "      <td>5.35</td>\n",
       "      <td>4.3</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>15</th>\n",
       "      <td>16</td>\n",
       "      <td>9.16</td>\n",
       "      <td>4.7</td>\n",
       "      <td>4.39</td>\n",
       "      <td>9.72</td>\n",
       "      <td>5.1</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>16</th>\n",
       "      <td>17</td>\n",
       "      <td>1.32</td>\n",
       "      <td>7.1</td>\n",
       "      <td>5.54</td>\n",
       "      <td>14.89</td>\n",
       "      <td>6.8</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>17</th>\n",
       "      <td>18</td>\n",
       "      <td>1.37</td>\n",
       "      <td>3.6</td>\n",
       "      <td>4.68</td>\n",
       "      <td>14.10</td>\n",
       "      <td>3.4</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>18</th>\n",
       "      <td>19</td>\n",
       "      <td>8.39</td>\n",
       "      <td>2.8</td>\n",
       "      <td>8.96</td>\n",
       "      <td>7.30</td>\n",
       "      <td>10.2</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th>19</th>\n",
       "      <td>20</td>\n",
       "      <td>2.43</td>\n",
       "      <td>8.0</td>\n",
       "      <td>7.36</td>\n",
       "      <td>13.83</td>\n",
       "      <td>7.8</td>\n",
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      "text/plain": [
       "    id    x1   x2    x3     x4     y\n",
       "0    1  6.80  2.4  8.81   2.82   3.6\n",
       "1    2  8.10  1.8  4.58  12.40  11.6\n",
       "2    3  3.11  9.3  5.88  12.23   5.7\n",
       "3    4  2.26  1.6  5.91   3.81   1.9\n",
       "4    5  6.03  4.2  6.84  10.75   4.0\n",
       "5    6  9.69  5.2  1.87   2.63   3.1\n",
       "6    7  8.59  6.7  1.26  11.99   7.1\n",
       "7    8  2.47  2.2  5.17  10.67   9.2\n",
       "8    9  9.38  4.4  6.41   9.54   3.1\n",
       "9   10  4.79  8.2  5.14  13.10   4.1\n",
       "10  11  3.57  7.5  9.15  14.92   9.7\n",
       "11  12  3.01  2.9  7.84  14.81   1.5\n",
       "12  13  4.11  2.9  8.99   6.27  11.1\n",
       "13  14  4.75  3.4  5.89   3.39  11.4\n",
       "14  15  2.59  9.7  7.95   5.35   4.3\n",
       "15  16  9.16  4.7  4.39   9.72   5.1\n",
       "16  17  1.32  7.1  5.54  14.89   6.8\n",
       "17  18  1.37  3.6  4.68  14.10   3.4\n",
       "18  19  8.39  2.8  8.96   7.30  10.2\n",
       "19  20  2.43  8.0  7.36  13.83   7.8"
      ]
     },
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   ],
   "source": [
    "import numpy as np\n",
    "import matplotlib.pyplot as plt\n",
    "import pandas as pd\n",
    "data = pd.read_csv(\"../input/mock-data-for-random-forest/MOCK_DATA.csv\")\n",
    "data\n"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
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    {
     "data": {
      "text/plain": [
       "DecisionTreeRegressor(max_depth=2, random_state=4)"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "x = data.iloc[:,1:5]\n",
    "y = data.iloc[:,-1]\n",
    "from sklearn.model_selection import train_test_split\n",
    "X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.5,random_state=55)\n",
    "\n",
    "from sklearn.tree import DecisionTreeRegressor\n",
    "\n",
    "regressor = DecisionTreeRegressor(random_state=4,max_depth=2)\n",
    "regressor.fit(X_train, y_train)\n",
    "\n",
    "\n"
   ]
  },
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    },
    "tags": []
   },
   "outputs": [],
   "source": [
    "from sklearn.tree import export_graphviz\n",
    "export_graphviz(regressor, out_file ='tree1.dot')\n",
    "\n",
    "import graphviz\n",
    "from IPython.display import display\n",
    "with open(\"tree1.dot\") as f:\n",
    "    dot_graph = f.read()\n",
    "    graphviz.Source(dot_graph).view()"
   ]
  },
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   "cell_type": "code",
   "execution_count": 4,
   "id": "fd146402",
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   "source": [
    "tmp = x.iloc[5:9,0].copy(deep=True)\n",
    "x.iloc[5:9,0] = x.iloc[5:9,1]\n",
    "x.iloc[5:9,1] = tmp\n"
   ]
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   "source": [
    "X_train, X_test, y_train, y_test = train_test_split( x, y, test_size=0.5,random_state=55)\n",
    "regressor_second = DecisionTreeRegressor(random_state=4,max_depth=2)\n",
    "regressor_second.fit(X_train, y_train)\n",
    "export_graphviz(regressor_second, out_file ='tree2.dot')\n",
    "with open(\"tree2.dot\") as f:\n",
    "    dot_graph = f.read()\n",
    "    graph=graphviz.Source(dot_graph,format=\"png\")\n",
    "    graph.view()"
   ]
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