regression_tree.ipynb

{"nbformat":4,"nbformat_minor":0,"metadata":{"colab":{"provenance":[],"authorship_tag":"ABX9TyOl07+nS2rX8vfh//fjUdrj"},"kernelspec":{"name":"python3","display_name":"Python 3"},"language_info":{"name":"python"}},"cells":[{"cell_type":"markdown","source":["### Installing and Importing necessary packages"],"metadata":{"id":"FGcrgz0VKk3D"}},{"cell_type":"code","execution_count":null,"metadata":{"id":"5GNAw99qDfma"},"outputs":[],"source":["!pip install pandas\n","!pip install scikit-learn\n","!pip install requests"]},{"cell_type":"code","source":["import pandas as pd\n","import requests\n","from sklearn.tree import DecisionTreeRegressor\n","from sklearn.model_selection import train_test_split"],"metadata":{"id":"jHzVxvmXKiq5"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Downloading dataset from IBM Object Storage"],"metadata":{"id":"cMZ5KleEK-cD"}},{"cell_type":"markdown","source":["A real estate company is planning to invest in Boston real estate. We have collected information about various areas of Boston and are tasked with created a model that can predict the median price of houses for that area so it can be used to make offers.\n","\n","The dataset had information on areas/towns not individual houses, the features are\n","\n","**CRIM:** Crime per capita\n","\n","**ZN:** Proportion of residential land zoned for lots over 25,000 sq.ft.\n","\n","**INDUS:** Proportion of non-retail business acres per town\n","\n","**CHAS:** Charles River dummy variable (= 1 if tract bounds river; 0 otherwise)\n","\n","**NOX:** Nitric oxides concentration (parts per 10 million)\n","\n","**RM:** Average number of rooms per dwelling\n","\n","**AGE:** Proportion of owner-occupied units built prior to 1940\n","\n","**DIS:** Weighted distances to ﬁve Boston employment centers\n","\n","**RAD:** Index of accessibility to radial highways\n","\n","**TAX:** Full-value property-tax rate per $10,000\n","\n","**PTRAIO:** Pupil-teacher ratio by town\n","\n","**LSTAT:** Percent lower status of the population\n","\n","**MEDV:** Median value of owner-occupied homes in $1000s\n"],"metadata":{"id":"03v7I26PLAOP"}},{"cell_type":"code","source":["def download(url, filename):\n","    response = requests.get(url)\n","    if response.status_code == 200:\n","        with open(filename, \"wb\") as f:\n","            f.write(response.content)"],"metadata":{"id":"G13g44azMLCz"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["dataset_path= \"https://cf-courses-data.s3.us.cloud-object-storage.appdomain.cloud/IBMDeveloperSkillsNetwork-ML0101EN-SkillsNetwork/labs/Module%203/data/real_estate_data.csv\"\n","download(dataset_path, 'real_estate_data.csv')\n"],"metadata":{"id":"fHVgri_pK8LF"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Exploring the dataset"],"metadata":{"id":"ETt5Yu8CMrhX"}},{"cell_type":"code","source":["df = pd.read_csv('real_estate_data.csv')\n","df.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"y80Xr4YbMtMU","executionInfo":{"status":"ok","timestamp":1704538884655,"user_tz":-210,"elapsed":438,"user":{"displayName":"Amir Kermanshahani","userId":"15909005352054600711"}},"outputId":"8fd35852-15a9-4a97-ee0c-a6d701c36a38"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n","0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n","1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n","2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n","3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n","4  0.06905   0.0   2.18   0.0  0.458  7.147  54.2  6.0622    3  222     18.7   \n","\n","   LSTAT  MEDV  \n","0   4.98  24.0  \n","1   9.14  21.6  \n","2   4.03  34.7  \n","3   2.94  33.4  \n","4    NaN  36.2  "],"text/html":["\n","  <div id=\"df-7fab3a73-28cf-43f3-97de-c74ab8cd0019\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","      <th>MEDV</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0.0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1</td>\n","      <td>296</td>\n","      <td>15.3</td>\n","      <td>4.98</td>\n","      <td>24.0</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>9.14</td>\n","      <td>21.6</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.02729</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>7.185</td>\n","      <td>61.1</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>4.03</td>\n","      <td>34.7</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.03237</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.998</td>\n","      <td>45.8</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>2.94</td>\n","      <td>33.4</td>\n","    </tr>\n","    <tr>\n","      <th>4</th>\n","      <td>0.06905</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>7.147</td>\n","      <td>54.2</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>NaN</td>\n","      <td>36.2</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" 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     0.081900    0.000000    5.190000    0.000000    0.449000    5.885500   \n","50%      0.253715    0.000000    9.690000    0.000000    0.538000    6.208500   \n","75%      3.560263   12.500000   18.100000    0.000000    0.624000    6.623500   \n","max     88.976200  100.000000   27.740000    1.000000    0.871000    8.780000   \n","\n","              AGE         DIS         RAD         TAX     PTRATIO       LSTAT  \\\n","count  486.000000  506.000000  506.000000  506.000000  506.000000  486.000000   \n","mean    68.518519    3.795043    9.549407  408.237154   18.455534   12.715432   \n","std     27.999513    2.105710    8.707259  168.537116    2.164946    7.155871   \n","min      2.900000    1.129600    1.000000  187.000000   12.600000    1.730000   \n","25%     45.175000    2.100175    4.000000  279.000000   17.400000    7.125000   \n","50%     76.800000    3.207450    5.000000  330.000000   19.050000   11.430000   \n","75%     93.975000    5.188425   24.000000  666.000000   20.200000   16.955000   \n","max    100.000000   12.126500   24.000000  711.000000   22.000000   37.970000   \n","\n","             MEDV  \n","count  506.000000  \n","mean    22.532806  \n","std      9.197104  \n","min      5.000000  \n","25%     17.025000  \n","50%     21.200000  \n","75%     25.000000  \n","max     50.000000  "],"text/html":["\n","  <div id=\"df-6bed909b-d9be-41c5-a1bc-49e9a473ae07\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","      <th>MEDV</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>count</th>\n","      <td>486.000000</td>\n","      <td>486.000000</td>\n","      <td>486.000000</td>\n","      <td>486.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>486.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>506.000000</td>\n","      <td>486.000000</td>\n","      <td>506.000000</td>\n","    </tr>\n","    <tr>\n","      <th>mean</th>\n","      <td>3.611874</td>\n","      <td>11.211934</td>\n","      <td>11.083992</td>\n","      <td>0.069959</td>\n","      <td>0.554695</td>\n","      <td>6.284634</td>\n","      <td>68.518519</td>\n","      <td>3.795043</td>\n","      <td>9.549407</td>\n","      <td>408.237154</td>\n","      <td>18.455534</td>\n","      <td>12.715432</td>\n","      <td>22.532806</td>\n","    </tr>\n","    <tr>\n","      <th>std</th>\n","      <td>8.720192</td>\n","      <td>23.388876</td>\n","      <td>6.835896</td>\n","      <td>0.255340</td>\n","      <td>0.115878</td>\n","      <td>0.702617</td>\n","      <td>27.999513</td>\n","      <td>2.105710</td>\n","      <td>8.707259</td>\n","      <td>168.537116</td>\n","      <td>2.164946</td>\n","      <td>7.155871</td>\n","      <td>9.197104</td>\n","    </tr>\n","    <tr>\n","      <th>min</th>\n","      <td>0.006320</td>\n","      <td>0.000000</td>\n","      <td>0.460000</td>\n","      <td>0.000000</td>\n","      <td>0.385000</td>\n","      <td>3.561000</td>\n","      <td>2.900000</td>\n","      <td>1.129600</td>\n","      <td>1.000000</td>\n","      <td>187.000000</td>\n","      <td>12.600000</td>\n","      <td>1.730000</td>\n","      <td>5.000000</td>\n","    </tr>\n","    <tr>\n","      <th>25%</th>\n","      <td>0.081900</td>\n","      <td>0.000000</td>\n","      <td>5.190000</td>\n","      <td>0.000000</td>\n","      <td>0.449000</td>\n","      <td>5.885500</td>\n","      <td>45.175000</td>\n","      <td>2.100175</td>\n","      <td>4.000000</td>\n","      <td>279.000000</td>\n","      <td>17.400000</td>\n","      <td>7.125000</td>\n","      <td>17.025000</td>\n","    </tr>\n","    <tr>\n","      <th>50%</th>\n","      <td>0.253715</td>\n","      <td>0.000000</td>\n","      <td>9.690000</td>\n","      <td>0.000000</td>\n","      <td>0.538000</td>\n","      <td>6.208500</td>\n","      <td>76.800000</td>\n","      <td>3.207450</td>\n","      <td>5.000000</td>\n","      <td>330.000000</td>\n","      <td>19.050000</td>\n","      <td>11.430000</td>\n","      <td>21.200000</td>\n","    </tr>\n","    <tr>\n","      <th>75%</th>\n","      <td>3.560263</td>\n","      <td>12.500000</td>\n","      <td>18.100000</td>\n","      <td>0.000000</td>\n","      <td>0.624000</td>\n","      <td>6.623500</td>\n","      <td>93.975000</td>\n","      <td>5.188425</td>\n","      <td>24.000000</td>\n","      <td>666.000000</td>\n","      <td>20.200000</td>\n","      <td>16.955000</td>\n","      <td>25.000000</td>\n","    </tr>\n","    <tr>\n","      <th>max</th>\n","      <td>88.976200</td>\n","      <td>100.000000</td>\n","      <td>27.740000</td>\n","      <td>1.000000</td>\n","      <td>0.871000</td>\n","      <td>8.780000</td>\n","      <td>100.000000</td>\n","      <td>12.126500</td>\n","      <td>24.000000</td>\n","      <td>711.000000</td>\n","      <td>22.000000</td>\n","      <td>37.970000</td>\n","      <td>50.000000</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-6bed909b-d9be-41c5-a1bc-49e9a473ae07')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            style=\"display:none;\">\n","\n","  <svg 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df[\"MEDV\"]"],"metadata":{"id":"jVSLUEorMNCB"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["X.head()"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":206},"id":"v7-YlwUgMX7B","executionInfo":{"status":"ok","timestamp":1704538950661,"user_tz":-210,"elapsed":338,"user":{"displayName":"Amir Kermanshahani","userId":"15909005352054600711"}},"outputId":"20107cfe-ac59-404d-bd34-ea5ab38320e2"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["      CRIM    ZN  INDUS  CHAS    NOX     RM   AGE     DIS  RAD  TAX  PTRATIO  \\\n","0  0.00632  18.0   2.31   0.0  0.538  6.575  65.2  4.0900    1  296     15.3   \n","1  0.02731   0.0   7.07   0.0  0.469  6.421  78.9  4.9671    2  242     17.8   \n","2  0.02729   0.0   7.07   0.0  0.469  7.185  61.1  4.9671    2  242     17.8   \n","3  0.03237   0.0   2.18   0.0  0.458  6.998  45.8  6.0622    3  222     18.7   \n","5  0.02985   0.0   2.18   0.0  0.458  6.430  58.7  6.0622    3  222     18.7   \n","\n","   LSTAT  \n","0   4.98  \n","1   9.14  \n","2   4.03  \n","3   2.94  \n","5   5.21  "],"text/html":["\n","  <div id=\"df-0e5e2ce9-7e92-4fd4-89cf-67056320d946\" class=\"colab-df-container\">\n","    <div>\n","<style scoped>\n","    .dataframe tbody tr th:only-of-type {\n","        vertical-align: middle;\n","    }\n","\n","    .dataframe tbody tr th {\n","        vertical-align: top;\n","    }\n","\n","    .dataframe thead th {\n","        text-align: right;\n","    }\n","</style>\n","<table border=\"1\" class=\"dataframe\">\n","  <thead>\n","    <tr style=\"text-align: right;\">\n","      <th></th>\n","      <th>CRIM</th>\n","      <th>ZN</th>\n","      <th>INDUS</th>\n","      <th>CHAS</th>\n","      <th>NOX</th>\n","      <th>RM</th>\n","      <th>AGE</th>\n","      <th>DIS</th>\n","      <th>RAD</th>\n","      <th>TAX</th>\n","      <th>PTRATIO</th>\n","      <th>LSTAT</th>\n","    </tr>\n","  </thead>\n","  <tbody>\n","    <tr>\n","      <th>0</th>\n","      <td>0.00632</td>\n","      <td>18.0</td>\n","      <td>2.31</td>\n","      <td>0.0</td>\n","      <td>0.538</td>\n","      <td>6.575</td>\n","      <td>65.2</td>\n","      <td>4.0900</td>\n","      <td>1</td>\n","      <td>296</td>\n","      <td>15.3</td>\n","      <td>4.98</td>\n","    </tr>\n","    <tr>\n","      <th>1</th>\n","      <td>0.02731</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>6.421</td>\n","      <td>78.9</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>9.14</td>\n","    </tr>\n","    <tr>\n","      <th>2</th>\n","      <td>0.02729</td>\n","      <td>0.0</td>\n","      <td>7.07</td>\n","      <td>0.0</td>\n","      <td>0.469</td>\n","      <td>7.185</td>\n","      <td>61.1</td>\n","      <td>4.9671</td>\n","      <td>2</td>\n","      <td>242</td>\n","      <td>17.8</td>\n","      <td>4.03</td>\n","    </tr>\n","    <tr>\n","      <th>3</th>\n","      <td>0.03237</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.998</td>\n","      <td>45.8</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>2.94</td>\n","    </tr>\n","    <tr>\n","      <th>5</th>\n","      <td>0.02985</td>\n","      <td>0.0</td>\n","      <td>2.18</td>\n","      <td>0.0</td>\n","      <td>0.458</td>\n","      <td>6.430</td>\n","      <td>58.7</td>\n","      <td>6.0622</td>\n","      <td>3</td>\n","      <td>222</td>\n","      <td>18.7</td>\n","      <td>5.21</td>\n","    </tr>\n","  </tbody>\n","</table>\n","</div>\n","    <div class=\"colab-df-buttons\">\n","\n","  <div class=\"colab-df-container\">\n","    <button class=\"colab-df-convert\" onclick=\"convertToInteractive('df-0e5e2ce9-7e92-4fd4-89cf-67056320d946')\"\n","            title=\"Convert this dataframe to an interactive table.\"\n","            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random_state=1)"],"metadata":{"id":"hhJrmdO-NRSp"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["### Classification Modeling"],"metadata":{"id":"DAO4kYwKNeSd"}},{"cell_type":"markdown","source":["Use squared error as the criterion."],"metadata":{"id":"nUKAeSxJarsN"}},{"cell_type":"code","source":["# regression_tree = DecisionTreeRegressor(criterion = 'squared_error')"],"metadata":{"id":"EWnR8LDzaLaq"},"execution_count":null,"outputs":[]},{"cell_type":"markdown","source":["Or use the Absolute Error for the criterion."],"metadata":{"id":"CyG8Qgnfaqlb"}},{"cell_type":"code","source":["regression_tree = DecisionTreeRegressor(criterion = 'absolute_error')"],"metadata":{"id":"NZVpDet6apn_"},"execution_count":null,"outputs":[]},{"cell_type":"code","source":["regression_tree.fit(X_train, Y_train)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/","height":74},"id":"BlXiBZAqaWdO","executionInfo":{"status":"ok","timestamp":1704538985082,"user_tz":-210,"elapsed":337,"user":{"displayName":"Amir Kermanshahani","userId":"15909005352054600711"}},"outputId":"634bc806-ba66-4118-8c88-7f3126cf09e6"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["DecisionTreeRegressor(criterion='absolute_error')"],"text/html":["<style>#sk-container-id-1 {color: black;background-color: white;}#sk-container-id-1 pre{padding: 0;}#sk-container-id-1 div.sk-toggleable {background-color: white;}#sk-container-id-1 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-1 label.sk-toggleable__label-arrow:before {content: \"▸\";float: left;margin-right: 0.25em;color: #696969;}#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {color: black;}#sk-container-id-1 div.sk-estimator:hover label.sk-toggleable__label-arrow:before {color: black;}#sk-container-id-1 div.sk-toggleable__content {max-height: 0;max-width: 0;overflow: hidden;text-align: left;background-color: #f0f8ff;}#sk-container-id-1 div.sk-toggleable__content pre {margin: 0.2em;color: black;border-radius: 0.25em;background-color: #f0f8ff;}#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {max-height: 200px;max-width: 100%;overflow: auto;}#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {content: \"▾\";}#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-estimator:hover {background-color: #d4ebff;}#sk-container-id-1 div.sk-parallel-item::after {content: \"\";width: 100%;border-bottom: 1px solid gray;flex-grow: 1;}#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {background-color: #d4ebff;}#sk-container-id-1 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-1 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-1 div.sk-item {position: relative;z-index: 1;}#sk-container-id-1 div.sk-parallel {display: flex;align-items: stretch;justify-content: center;background-color: white;position: relative;}#sk-container-id-1 div.sk-item::before, #sk-container-id-1 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-1 div.sk-parallel-item {display: flex;flex-direction: column;z-index: 1;position: relative;background-color: white;}#sk-container-id-1 div.sk-parallel-item:first-child::after {align-self: flex-end;width: 50%;}#sk-container-id-1 div.sk-parallel-item:last-child::after {align-self: flex-start;width: 50%;}#sk-container-id-1 div.sk-parallel-item:only-child::after {width: 0;}#sk-container-id-1 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-1 div.sk-label label {font-family: monospace;font-weight: bold;display: inline-block;line-height: 1.2em;}#sk-container-id-1 div.sk-label-container {text-align: center;}#sk-container-id-1 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-1 div.sk-text-repr-fallback {display: none;}</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>DecisionTreeRegressor(criterion=&#x27;absolute_error&#x27;)</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-1\" type=\"checkbox\" checked><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label sk-toggleable__label-arrow\">DecisionTreeRegressor</label><div class=\"sk-toggleable__content\"><pre>DecisionTreeRegressor(criterion=&#x27;absolute_error&#x27;)</pre></div></div></div></div></div>"]},"metadata":{},"execution_count":19}]},{"cell_type":"markdown","source":["### Evaluation"],"metadata":{"id":"ZNHHViSQN35J"}},{"cell_type":"code","source":["regression_tree.score(X_test, Y_test)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"Rn2V7XAON43I","executionInfo":{"status":"ok","timestamp":1704538987755,"user_tz":-210,"elapsed":580,"user":{"displayName":"Amir Kermanshahani","userId":"15909005352054600711"}},"outputId":"75804255-489e-4509-fb5b-ce69334744c3"},"execution_count":null,"outputs":[{"output_type":"execute_result","data":{"text/plain":["0.874674505128397"]},"metadata":{},"execution_count":20}]},{"cell_type":"code","source":["prediction = regression_tree.predict(X_test)\n","\n","print(\"$\",(prediction - Y_test).abs().mean()*1000)"],"metadata":{"colab":{"base_uri":"https://localhost:8080/"},"id":"lQpE7GJRagHa","executionInfo":{"status":"ok","timestamp":1704538991195,"user_tz":-210,"elapsed":316,"user":{"displayName":"Amir Kermanshahani","userId":"15909005352054600711"}},"outputId":"62cb95e6-9526-49c0-b8b2-dd08840d3eba"},"execution_count":null,"outputs":[{"output_type":"stream","name":"stdout","text":["$ 2567.0886075949365\n"]}]}]}