{"metadata":{"kernelspec":{"language":"python","display_name":"Python 3","name":"python3"},"language_info":{"name":"python","version":"3.10.13","mimetype":"text/x-python","codemirror_mode":{"name":"ipython","version":3},"pygments_lexer":"ipython3","nbconvert_exporter":"python","file_extension":".py"},"kaggle":{"accelerator":"none","dataSources":[{"sourceId":8552606,"sourceType":"datasetVersion","datasetId":4965286}],"dockerImageVersionId":30698,"isInternetEnabled":true,"language":"python","sourceType":"notebook","isGpuEnabled":false}},"nbformat_minor":4,"nbformat":4,"cells":[{"cell_type":"markdown","source":"A **regression decision tree**, akin to its classification counterpart, is a potent tool in machine learning, particularly well-suited for **regression tasks**. Operating on a similar principle to a series of **if-then rules**, it offers **transparency** and **interpretability**, making it intuitive for humans to grasp. At each node of the tree, the algorithm evaluates a feature and makes a decision based on a set of conditions, branching the data into **smaller subsets**. This recursive process continues until a stopping criterion is reached, forming a tree structure where each leaf node represents a predicted continuous value.\n\nTraversing from the root to a leaf node, one can trace the **decision-making process** of the model in terms of if-then rules, elucidating how it predicts outcomes. This transparency and simplicity are invaluable for understanding the **underlying logic behind regression decisions**, making regression decision trees essential for tasks such as predictive modeling and trend analysis.","metadata":{}},{"cell_type":"markdown","source":"### 1.Read the Data  \nWe read a version of the House Price data that has been preprocessed in pipeline 3 in the linked notebook.\nhttps://www.kaggle.com/code/zahrazolghadr/houseprice-competition-pipeline","metadata":{}},{"cell_type":"code","source":"import pandas as pd\nimport numpy as np\n\nX_data = pd.read_csv('/kaggle/input/house-price-ready-to-modeling/X_HousePrice.csv')\nX_data = X_data.set_index(['Unnamed: 0', 'Unnamed: 1'])\nX_data.info()\n\nX_train = X_data.loc['train']\nX_test = X_data.loc['test']\n\ny_data = pd.read_csv('/kaggle/input/house-price-ready-to-modeling/y_HousePrice.csv')\ny_data = y_data.set_index(['Unnamed: 0', 'Id'])\ny_data.info()\n\ny_train = y_data.loc['train']\ny_test = y_data.loc['test']\n\ny_train_log = np.log(y_train.copy())\ny_test_log = np.log(y_test.copy())","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:27.560283Z","iopub.execute_input":"2024-06-06T14:47:27.560672Z","iopub.status.idle":"2024-06-06T14:47:28.807947Z","shell.execute_reply.started":"2024-06-06T14:47:27.560640Z","shell.execute_reply":"2024-06-06T14:47:28.806844Z"},"trusted":true},"execution_count":1,"outputs":[{"name":"stdout","text":"<class 'pandas.core.frame.DataFrame'>\nMultiIndex: 1439 entries, ('train', 0) to ('test', 437)\nData columns (total 42 columns):\n #   Column                         Non-Null Count  Dtype  \n---  ------                         --------------  -----  \n 0   num__LotFrontage               1439 non-null   float64\n 1   num__LotArea                   1439 non-null   float64\n 2   num__OverallQual               1439 non-null   float64\n 3   num__OverallCond               1439 non-null   float64\n 4   num__BsmtUnfSF                 1439 non-null   float64\n 5   num__TotalBsmtSF               1439 non-null   float64\n 6   num__1stFlrSF                  1439 non-null   float64\n 7   num__GrLivArea                 1439 non-null   float64\n 8   num__BsmtFullBath              1439 non-null   float64\n 9   num__HalfBath                  1439 non-null   float64\n 10  num__TotRmsAbvGrd              1439 non-null   float64\n 11  num__Fireplaces                1439 non-null   float64\n 12  num__GarageCars                1439 non-null   float64\n 13  num__MoSold                    1439 non-null   float64\n 14  num__AgeBuilt                  1439 non-null   float64\n 15  num__AgeRemodAdd               1439 non-null   float64\n 16  num__AgeGarageBlt              1439 non-null   float64\n 17  num__AgeSold                   1439 non-null   float64\n 18  nom__MSZoning_RM               1439 non-null   float64\n 19  nom__LotShape_Reg              1439 non-null   float64\n 20  nom__LandContour_Not Lvl       1439 non-null   float64\n 21  nom__LandSlope_Not Gtl         1439 non-null   float64\n 22  nom__Neighborhood_CollgCr      1439 non-null   float64\n 23  nom__Neighborhood_Sawyer       1439 non-null   float64\n 24  nom__RoofStyle_Not Gable       1439 non-null   float64\n 25  nom__Exterior2nd_Stucco        1439 non-null   float64\n 26  nom__GarageType_Attchd         1439 non-null   float64\n 27  nom__GarageType_Basment        1439 non-null   float64\n 28  nom__Fence_non-existent        1439 non-null   float64\n 29  nom__SaleCondition_Not Normal  1439 non-null   float64\n 30  ord__MSSubClass                1439 non-null   float64\n 31  ord__ExterQual                 1439 non-null   float64\n 32  ord__BsmtQual                  1439 non-null   float64\n 33  ord__BsmtExposure              1439 non-null   float64\n 34  ord__BsmtFinType1              1439 non-null   float64\n 35  ord__BsmtFinSF1                1439 non-null   float64\n 36  ord__BsmtFinType2              1439 non-null   float64\n 37  ord__KitchenQual               1439 non-null   float64\n 38  ord__FireplaceQu               1439 non-null   float64\n 39  ord__GarageArea                1439 non-null   float64\n 40  ord__GarageCond                1439 non-null   float64\n 41  ord__OpenPorchSF               1439 non-null   float64\ndtypes: float64(42)\nmemory usage: 516.7+ KB\n<class 'pandas.core.frame.DataFrame'>\nMultiIndex: 1439 entries, ('train', 708) to ('test', 1121)\nData columns (total 1 columns):\n #   Column     Non-Null Count  Dtype\n---  ------     --------------  -----\n 0   SalePrice  1439 non-null   int64\ndtypes: int64(1)\nmemory usage: 59.2+ KB\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 2.Fit the Model and Configure Parameters, then Predict Test Set\nHere you can see how to fit the DecisionTreeRegressor model and configure its parameters.\n\n#### Parameters Explanation:\n\n* #### criterion:   \nThe criterion parameter defines the function to measure the quality of a split in a decision tree. For regression trees, it's typically set to **\"squared_error\"** to minimize the mean squared error. It can be set to **“friedman_mse”** or **“absolute_error”**.\n\n* #### max_depth:  \nThe max_depth parameter specifies the maximum depth of the decision tree. It limits the number of nodes in the tree, helping to prevent overfitting and controlling the complexity of the model.\n\n* #### min_samples_split:  \nThe min_samples_split parameter sets the minimum number of samples required to split an internal node. If the number of samples at a node is less than this value, the node will not be split further.\n\n* #### min_samples_leaf:  \nThe min_samples_leaf parameter specifies the minimum number of samples required to be at a leaf node. It prevents the creation of nodes that represent very few samples, which can help to avoid overfitting.\n\n* #### min_weight_fraction_leaf:  \nThe min_weight_fraction_leaf parameter is similar to min_samples_leaf but instead represents the minimum weighted fraction of the sum total of weights required to be at a leaf node. It is useful when working with weighted data.\n\n* #### random_state:  \nThe random_state parameter sets the seed for random number generation. It ensures reproducibility of results when the algorithm involves randomization, such as random splits.\n\n* #### min_impurity_decrease:  \nThe min_impurity_decrease parameter specifies the minimum impurity decrease required for a split to happen. It helps control the growth of the tree by only allowing splits that reduce impurity by a certain amount.\n\n* #### ccp_alpha:  \nThe ccp_alpha parameter is the complexity parameter used for Minimal Cost-Complexity Pruning. It represents the complexity threshold. Higher values lead to more pruning, resulting in simpler trees.","metadata":{}},{"cell_type":"code","source":"!pip install --upgrade scikit-learn","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:28.809698Z","iopub.execute_input":"2024-06-06T14:47:28.810016Z","iopub.status.idle":"2024-06-06T14:47:47.970077Z","shell.execute_reply.started":"2024-06-06T14:47:28.809988Z","shell.execute_reply":"2024-06-06T14:47:47.968767Z"},"trusted":true},"execution_count":2,"outputs":[{"name":"stdout","text":"Requirement already satisfied: scikit-learn in /opt/conda/lib/python3.10/site-packages (1.2.2)\nCollecting scikit-learn\n  Downloading scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl.metadata (11 kB)\nRequirement already satisfied: numpy>=1.19.5 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.26.4)\nRequirement already satisfied: scipy>=1.6.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.11.4)\nRequirement already satisfied: joblib>=1.2.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (1.4.0)\nRequirement already satisfied: threadpoolctl>=3.1.0 in /opt/conda/lib/python3.10/site-packages (from scikit-learn) (3.2.0)\nDownloading scikit_learn-1.5.0-cp310-cp310-manylinux_2_17_x86_64.manylinux2014_x86_64.whl (13.3 MB)\n\u001b[2K   \u001b[90m━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━━\u001b[0m \u001b[32m13.3/13.3 MB\u001b[0m \u001b[31m57.2 MB/s\u001b[0m eta \u001b[36m0:00:00\u001b[0m00:01\u001b[0m00:01\u001b[0m\n\u001b[?25hInstalling collected packages: scikit-learn\n  Attempting uninstall: scikit-learn\n    Found existing installation: scikit-learn 1.2.2\n    Uninstalling scikit-learn-1.2.2:\n      Successfully uninstalled scikit-learn-1.2.2\n\u001b[31mERROR: pip's dependency resolver does not currently take into account all the packages that are installed. This behaviour is the source of the following dependency conflicts.\nspopt 0.6.0 requires shapely>=2.0.1, but you have shapely 1.8.5.post1 which is incompatible.\u001b[0m\u001b[31m\n\u001b[0mSuccessfully installed scikit-learn-1.5.0\n","output_type":"stream"}]},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.metrics import root_mean_squared_error, r2_score\n\n# Instantiate the DecisionTreeRegressor\nregression_tree = DecisionTreeRegressor(criterion='squared_error', max_depth=3,\n                                        min_samples_split=30, min_samples_leaf=15, min_weight_fraction_leaf=0.0,\n                                        random_state=111, min_impurity_decrease=0.0, ccp_alpha=0.0)\n\n# Train the model\nregression_tree.fit(X_train, y_train_log)\n\n# Predict on the test set\ny_pred = regression_tree.predict(X_test)\n\n# Evaluate the model using Mean Squared Error (MSE)\nrmse = root_mean_squared_error(y_test_log, y_pred)\nprint(\"Root Mean Squared Error:\", rmse)\n\n# R-squared (R2) score\nr2 = r2_score(y_test_log, y_pred)\nprint(\"R-squared (R2) score:\", r2)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:47.976106Z","iopub.execute_input":"2024-06-06T14:47:47.976770Z","iopub.status.idle":"2024-06-06T14:47:48.681574Z","shell.execute_reply.started":"2024-06-06T14:47:47.976731Z","shell.execute_reply":"2024-06-06T14:47:48.680192Z"},"trusted":true},"execution_count":3,"outputs":[{"name":"stdout","text":"Root Mean Squared Error: 0.29736287449195453\nR-squared (R2) score: 0.42730893450570306\n","output_type":"stream"}]},{"cell_type":"markdown","source":"The DecisionTreeRegressor model **attribues**:","metadata":{}},{"cell_type":"code","source":"regression_tree.feature_importances_\nregression_tree.max_features_\nregression_tree.n_features_in_\nregression_tree.feature_names_in_\nregression_tree.n_outputs_\nregression_tree.tree_","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:48.682990Z","iopub.execute_input":"2024-06-06T14:47:48.683614Z","iopub.status.idle":"2024-06-06T14:47:48.692128Z","shell.execute_reply.started":"2024-06-06T14:47:48.683575Z","shell.execute_reply":"2024-06-06T14:47:48.690966Z"},"trusted":true},"execution_count":4,"outputs":[{"execution_count":4,"output_type":"execute_result","data":{"text/plain":"<sklearn.tree._tree.Tree at 0x7ee869496aa0>"},"metadata":{}}]},{"cell_type":"markdown","source":"### 3.Check the Overfitting\n\nOverfitting occurs when a machine learning model **learns the training data too well**, capturing noise or random fluctuations that are not representative of the true underlying patterns. This can happen when the **complexity** of the model is excessive, such as with a decision tree having a large depth. As a result, the model may **memorize** the training data, leading to reduced training error. However, beyond a certain point, this excessive complexity can cause the model **to perform poorly on unseen test data**, as it fails to generalize well and exhibits increased error.  \nTo investigate whether the model is **overfit** or not, you can compare the evaluation metrics between the training and test sets. If the difference is noteworthy, the model may be overfitted.","metadata":{}},{"cell_type":"code","source":"import matplotlib.pyplot as plt\nfrom sklearn.tree import DecisionTreeRegressor\nfrom sklearn.metrics import root_mean_squared_error\n\n\n# Initialize arrays to store train and test errors\ntrain_rmses = []\ntest_rmses = []\n\n# Define a range of max_depth values\nmax_depths = range(1, 8)\n\n# Iterate over max_depth values\nfor max_depth in max_depths:\n    # Initialize and train the regressor\n    clf = DecisionTreeRegressor(max_depth=max_depth, min_samples_split=40, min_samples_leaf=20, random_state=111)\n    clf.fit(X_train, y_train_log)\n    \n    # Predictions on train and test sets\n    y_train_pred = clf.predict(X_train)\n    y_test_pred = clf.predict(X_test)\n    \n    # Calculate mse for train and test sets\n    train_rmse = root_mean_squared_error(y_train_log, y_train_pred)\n    test_rmse = root_mean_squared_error(y_test_log, y_test_pred)\n\n    \n    # Append mses to the respective lists\n    train_rmses.append(train_rmse)\n    test_rmses.append(test_rmse)\n\n# Plot train and test MSE versus max_depth\nplt.figure(figsize=(10, 6))\nplt.plot(max_depths, train_rmses, label='Train RMSE', marker='o')\nplt.plot(max_depths, test_rmses, label='Test RMSE', marker='o')\nplt.title('RMSE vs Max Depth for Regression Tree')\nplt.xlabel('Max Depth')\nplt.ylabel('root_mean_squared_error')\nplt.xticks(max_depths)\nplt.legend()\nplt.grid(True)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:48.693850Z","iopub.execute_input":"2024-06-06T14:47:48.694242Z","iopub.status.idle":"2024-06-06T14:47:49.139673Z","shell.execute_reply.started":"2024-06-06T14:47:48.694199Z","shell.execute_reply":"2024-06-06T14:47:49.138472Z"},"trusted":true},"execution_count":5,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 1000x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"code","source":"# Predict on the training set\ny_train_pred = regression_tree.predict(X_train)\n\n# Predict on the test set\ny_test_pred = regression_tree.predict(X_test)\n\n# Calculate R2 for the train and test sets\nr2_train = r2_score(y_train_log, y_train_pred)\nr2_test = r2_score(y_test_log, y_test_pred)\n\n# Calculate the difference in R2 between train and test sets\nr2_diff = r2_train - r2_test\n\nprint(\"R-squared (R2) for train set:\", r2_train)\nprint(\"R-squared (R2) for test set:\", r2_test)\nprint(\"Difference in R2 between train and test sets:\", r2_diff)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:49.140951Z","iopub.execute_input":"2024-06-06T14:47:49.141310Z","iopub.status.idle":"2024-06-06T14:47:49.157806Z","shell.execute_reply.started":"2024-06-06T14:47:49.141279Z","shell.execute_reply":"2024-06-06T14:47:49.156563Z"},"trusted":true},"execution_count":6,"outputs":[{"name":"stdout","text":"R-squared (R2) for train set: 0.7183731323498757\nR-squared (R2) for test set: 0.42730893450570306\nDifference in R2 between train and test sets: 0.2910641978441726\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 4.Evaluate the Model\nTo assess regression performance beyond basic metrics like R-squared (R2) score, you can utilize various evaluation metrics to gain insight into different aspects of the model's predictive capability.\n\n* #### R-squared (R2) score:  \nThis metric measures the proportion of the variance in the dependent variable that is predictable from the independent variables. It ranges from 0 to 1, where 1 indicates a perfect fit.\n\n* #### Mean Absolute Error (MAE):  \nMAE represents the average absolute difference between the predicted and actual values. It provides a measure of the average magnitude of errors without considering their direction.\n\n* #### Mean Squared Error (MSE):   \nMSE calculates the average of the squares of the errors between the predicted and actual values. It penalizes larger errors more heavily than smaller ones.  \n\n* #### Root Mean Squared Error (RMSE): \nRMSE is calculated by taking the square root of the mean squared error (MSE). In simpler terms, it's the average magnitude of the errors, on the same scale as the original data, making it easier to interpret.\n\n* #### Mean Absolute Percentage Error (MAPE):   \nMAPE measures the average absolute percentage difference between the predicted and actual values, providing insight into the accuracy of the predictions relative to the actual values.\n\n* #### Median Absolute Error:   \nThis metric represents the median of the absolute differences between the predicted and actual values. It is less sensitive to outliers compared to MAE and MSE.\n\n* #### Max Error:   \nMax Error computes the maximum absolute error between the predicted and actual values, indicating the worst-case scenario error.\n\n* #### Correlation between predicted and real values:   \nThis metric quantifies the linear relationship between the predicted and actual values, providing insight into the overall predictive performance of the model.","metadata":{}},{"cell_type":"code","source":"import numpy as np\nfrom sklearn.metrics import r2_score, mean_absolute_error, mean_squared_error, root_mean_squared_error, median_absolute_error,\\\n                            max_error, mean_absolute_percentage_error\n\n# R-squared (R2) score\nr2 = r2_score(y_test_log, y_pred)\n\n# Mean Absolute Error (MAE)\nmae = mean_absolute_error(y_test_log, y_pred)\n\n# Mean Squared Error (MSE)\nmse = mean_squared_error(y_test_log, y_pred)  \n\n# Root Mean Squared Error (RMSE)\nrmse = root_mean_squared_error(y_test_log, y_pred)\n\n# Mean Absolute Percentage Error (MAPE)\n# Avoiding division by zero\ny_test_values = y_test_log.to_numpy()\ny_test_values = y_test_values.reshape(y_test_values.shape[0])\nmape = np.mean(np.abs((y_test_values - y_pred) / np.where(y_test_values == 0, 1, y_test_values))) * 100\n\n# Median Absolute Error\nmedae = median_absolute_error(y_test_log, y_pred)\n\n# Max Error\nmax_err = max_error(y_test_log, y_pred)\n\n# Correlation between predicted and real values\ncorr = np.corrcoef(y_test_values, y_pred)[0, 1]\n\nprint(\"R-squared (R2) score:\", r2)\nprint(\"Mean Absolute Error (MAE):\", mae)\nprint(\"Mean Squared Error (MSE):\", mse)\nprint(\"Root Mean Squared Error (RMSE):\", rmse)\nprint(\"Mean Absolute Percentage Error (MAPE):\", mape)\nprint(\"Median Absolute Error:\", medae)\nprint(\"Max Error:\", max_err)\nprint(\"Correlation between predicted and real values:\", corr)","metadata":{"trusted":true},"execution_count":20,"outputs":[{"execution_count":20,"output_type":"execute_result","data":{"text/plain":"array([11.8493977 , 11.7905572 , 12.95953852, 12.47609978, 11.9511804 ,\n       12.88029182, 11.7745202 , 11.96356787, 11.41861479, 11.77721096,\n       12.52416267, 11.84868316, 12.00701176, 12.31043266, 11.98292909,\n       11.94470788, 11.84222921, 12.38755635, 12.53537639, 11.74006104,\n       11.87059991, 12.42801548, 11.62625415, 12.887127  , 11.88448902,\n       11.31447453, 12.10071213, 11.9511804 , 11.36210258, 11.90090526,\n       11.62625415, 11.8493977 , 12.03171926, 12.68849879, 12.04355372,\n       11.86358234, 12.93675161, 12.20607265, 11.92503512, 12.1388639 ,\n       12.32385568, 11.46163217, 11.66134547, 12.92999148, 12.30138283,\n       10.54270639, 12.66539443, 12.34583459, 11.49272276, 11.8313792 ,\n       12.32163099, 11.96718074, 11.42954386, 12.09884928, 11.88103479,\n       12.06527492, 11.75587164, 11.58896015, 12.52452638, 11.7352687 ,\n       12.09793049, 11.60823564, 11.79810441, 12.24047407, 11.99535161,\n       10.91508846, 12.01460943, 11.97665948, 11.92171836, 12.99175343,\n       12.38421883, 11.69524702, 11.84222921, 12.04941884, 12.19551713,\n       11.33260191, 11.85082525, 12.01733052, 11.56171563, 11.44571672,\n       12.06104687, 11.65268741, 12.74444437, 11.95697006, 11.45105006,\n       11.91505167, 12.2067774 , 11.27720313, 11.59910316, 11.54829261,\n       11.81154748, 12.18586994, 12.66032792, 11.91170158, 12.46651198,\n       12.07823927, 12.08390501, 11.38509209, 12.90669184, 12.01370075,\n       12.66191396, 11.9511804 , 11.57119437, 11.8493977 , 12.00150548,\n       12.56024446, 11.7905572 , 11.6483301 , 12.4292162 , 11.87756858,\n       11.84222921, 12.08069081, 11.88621167, 12.16597955, 12.3883942 ,\n       12.06681058, 11.60823564, 12.07823927, 11.99843328, 12.36788533,\n       12.46843691, 11.89818787, 11.4114463 , 12.47800636, 12.7512997 ,\n       11.9103584 , 11.82407989, 12.12269104, 11.69524702, 11.70354582,\n       12.63101353, 11.88448902, 11.87756858, 12.04941884, 11.73606902,\n       12.05980333, 11.65268741, 11.54442413, 12.25486281, 12.11176197,\n       12.52088339, 11.91839057, 12.10071213, 11.41861479, 11.54248427,\n       11.28978191, 12.05524976, 12.1388639 , 11.49882654, 11.8493977 ,\n       12.10625231, 12.1281111 , 11.97665948, 11.7745202 , 12.4292162 ,\n       12.04355372, 12.72188581, 12.19095901, 11.68687877, 12.52452638,\n       13.12603064, 12.10625231, 11.65268741, 12.8583197 , 12.17498953,\n       12.82772919, 12.13215696, 12.19551713, 12.14419724, 11.68568478,\n       12.15477935, 13.32392695, 11.69940503, 12.40656151, 11.8386256 ,\n       11.81303006, 11.58058411, 11.73606902, 12.48582713, 11.55214618,\n       12.01370075, 12.02873863, 12.07823927, 12.70076889, 10.46024211,\n       12.32385568, 12.32385568, 12.87901712, 11.70766954, 11.27720313,\n       11.56646623, 12.72188581, 12.12214741, 11.74403719, 11.31447453,\n       12.08953883, 12.45293272, 11.92503512, 11.89818787, 12.07254125,\n       11.75978554, 11.75587164, 12.05379521, 12.29910751, 12.90420737,\n       11.94194433, 12.10071213, 12.79135618, 12.14950229, 12.49874226,\n       11.94145585, 12.18075484, 12.11121236, 12.04060821, 11.45899712,\n       12.50539916, 11.75194237, 11.8493977 , 11.84222921, 11.76756768,\n       11.79810441, 12.03469103, 11.98292909, 11.76756768, 11.88448902,\n       12.21106019, 12.33710091, 12.23076526, 11.73606902, 12.56723749,\n       11.8968264 , 11.86709728, 11.81672692, 12.14286657, 12.17303279,\n       11.8493977 , 12.22587527, 12.49125159, 11.97350987, 12.10071213,\n       12.17303279, 11.73606902, 11.87756858, 12.72486644, 11.49577931,\n       11.69940503, 12.44862659, 11.51292546, 12.06681058, 11.75587164,\n       12.00150548, 12.18280402, 11.9316358 , 11.40756495, 11.26446411,\n       12.10348606, 12.03171926, 12.66980666, 11.72803684, 11.95761129,\n       11.71177632, 12.07823927, 12.17921519, 11.65268741, 12.05815252,\n       11.94470788, 12.52452638, 12.24961095, 12.12269104, 12.05815252,\n       12.07823927, 12.47716791, 11.9511804 , 12.18586994, 11.71993963,\n       11.80559508, 11.00209984, 11.99226042, 12.27839331, 11.8493977 ,\n       12.27134527, 11.98417831, 11.97035031, 11.48246626, 11.8196746 ,\n       11.87409031, 12.09368751, 11.77143616, 11.92503512, 12.20607265,\n       11.91638857, 12.64915462, 11.35627165, 12.27373129, 12.00456827,\n       11.9316358 , 12.07254125, 11.53076538, 12.62806706, 11.96400108,\n       12.09737323, 12.24288662, 11.65268741, 12.23076526, 12.24769432,\n       11.6784399 , 12.06968002, 12.92391244, 11.98292909, 11.58431546,\n       11.69524702, 12.01672647, 12.38421883, 12.1281111 , 12.08953883,\n       12.34583459, 11.32055357, 12.10699237, 12.20607265, 12.65973409,\n       12.23076526, 12.02574909, 11.79433792, 11.94145585, 13.27582754,\n       11.6784399 , 12.84792653, 12.01974307, 11.79433792, 11.58524613,\n       12.45254202, 12.16785143, 11.81303006, 11.56076279, 11.91371298,\n       11.73606902, 11.78676213, 12.45410391, 11.60823564, 11.79433792,\n       12.22096126, 12.50617724, 11.90834024, 11.44035477, 11.91170158,\n       12.65235975, 11.77528973, 11.98292909, 11.8313792 , 11.99226042,\n       12.28765263, 12.20657252, 12.25961341, 12.35233515, 11.79810441,\n       11.9316358 , 11.97035031, 12.33655251, 11.68657621, 12.28303369,\n       11.34450681, 12.67607627, 11.68687877, 12.15477935, 11.81303006,\n       11.97665948, 12.1388639 , 12.0917835 , 11.58058411, 11.98292909,\n       12.09514108, 12.5776362 , 11.72803684, 12.27373129, 12.12269104,\n       11.1124479 , 12.49125159, 11.69940503, 12.03765399, 12.36734079,\n       12.17044547, 11.98292909, 12.03469103, 11.87756858, 11.69524702,\n       12.02873863, 11.42409425, 11.77528973, 11.87234663, 11.8493977 ,\n       12.18841771, 11.58988651, 11.72399644, 11.87059991, 11.9316358 ,\n       12.07254125, 11.85651517, 11.84222921, 11.65268741, 11.87756858,\n       12.10348606, 12.72932079, 11.98292909, 11.60367983, 12.08390501,\n       12.08672589, 12.76568843, 12.25486281, 12.10071213, 11.86358234,\n       11.36210258, 11.89818787, 11.75194237, 11.9511804 , 12.34583459,\n       11.81303006, 12.88567095, 11.7829526 , 12.72783821, 11.51292546,\n       11.73606902, 11.88448902, 11.73606902, 12.14950229, 12.16525065,\n       11.92337811, 11.87059991, 11.9511804 , 11.73206099, 12.69158046,\n       12.35978037, 11.89818787, 12.10625231, 11.82407989, 12.72188581,\n       12.10015642, 12.50617724, 11.681824  ])"},"metadata":{}}]},{"cell_type":"code","source":"import matplotlib.pyplot as plt\n\nplt.figure(figsize=(8, 6))\nplt.scatter(y_test_log, y_pred, color='blue', alpha=0.5)\nplt.plot([y_test_log.min(), y_test_log.max()], [y_test_log.min(), y_test_log.max()], 'k--', lw=2)  # Plot the diagonal line\nplt.xlabel('True Values')\nplt.ylabel('Predicted Values')\nplt.title('Predicted Values vs True Values')\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:49.186265Z","iopub.execute_input":"2024-06-06T14:47:49.186639Z","iopub.status.idle":"2024-06-06T14:47:49.473863Z","shell.execute_reply.started":"2024-06-06T14:47:49.186607Z","shell.execute_reply":"2024-06-06T14:47:49.472727Z"},"trusted":true},"execution_count":8,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x600 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### 5. Hyperparameters Tuning  \nTo fine-tune the hyperparameters of the DecisionTreeRegressor model, we'll set specific values for each parameter and use techniques like grid search along with cross-validation to identify the optimal parameter set. By optimizing the hyperparameters, we aim to improve the model's predictive performance on unseen data.   \nSee the scoring parameter: defining model evaluation rules https://scikit-learn.org/stable/modules/model_evaluation.html#scoring-parameter","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import DecisionTreeRegressor\nfrom sklearn.model_selection import GridSearchCV\n\n\n# Define the parameter grid to search\nparam_grid = {\n    'criterion': ['squared_error', 'friedman_mse', 'absolute_error'],\n    'max_depth': [2, 3, 4, 5, 6], \n    'min_samples_split': [30, 60, 90],  \n    'min_samples_leaf': [15, 30, 45],\n    'ccp_alpha': [0, 0.5, 1]\n}\n\n# Instantiate the DecisionTreeRegressor\nregression_tree = DecisionTreeRegressor(random_state=111)\n\n# Create GridSearchCV object\ngrid_search = GridSearchCV(regression_tree, param_grid, scoring='r2', cv=5)\n#'neg_root_mean_squared_error'\n# Fit the grid search to the data\ngrid_search.fit(X_train, y_train_log)\n\n# Get the best estimator\nbest_reg_tree = grid_search.best_estimator_\n\n# Get the best parameters\nbest_params = grid_search.best_params_\nprint(\"Best Parameters:\", best_params)\n","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:47:49.477562Z","iopub.execute_input":"2024-06-06T14:47:49.477946Z","iopub.status.idle":"2024-06-06T14:48:44.612649Z","shell.execute_reply.started":"2024-06-06T14:47:49.477916Z","shell.execute_reply":"2024-06-06T14:48:44.609116Z"},"trusted":true},"execution_count":9,"outputs":[{"name":"stdout","text":"Best Parameters: {'ccp_alpha': 0, 'criterion': 'friedman_mse', 'max_depth': 6, 'min_samples_leaf': 15, 'min_samples_split': 30}\n","output_type":"stream"}]},{"cell_type":"code","source":"\n# Train the regressor with the full training data\nbest_reg_tree.fit(X_train, y_train_log)\n\n# Predict the target for the train set\ny_pred_train = best_reg_tree.predict(X_train)\n\n# Predict the target for the test set\ny_pred = best_reg_tree.predict(X_test)\n\n# R-squared (R2) score for training set\nr2_train = r2_score(y_train_log, y_pred_train)\n\n# Mean Absolute Error (MAE) for training set\nmae_train = mean_absolute_error(y_train_log, y_pred_train)\n\n# Mean Squared Error (MSE) for training set\nmse_train = mean_squared_error(y_train_log, y_pred_train)\n\n# Root Mean Squared Error (RMSE) for training set\nrmse_train = root_mean_squared_error(y_train_log, y_pred_train)\n\n# Mean Absolute Percentage Error (MAPE) for training set\n# Avoiding division by zero\ny_train_values = y_train_log.to_numpy()\ny_train_values = y_train_values.reshape(y_train_values.shape[0])\nmape_train = np.mean(np.abs((y_train_values - y_pred_train) / np.where(y_train_values == 0, 1, y_train_values))) * 100\n\n# Median Absolute Error for training set\nmedae_train = median_absolute_error(y_train_log, y_pred_train)\n\n# Max Error for training set\nmax_err_train = max_error(y_train_log, y_pred_train)\n\n# Correlation between predicted and real values for training set\ncorr_train = np.corrcoef(y_train_values, y_pred_train)[0, 1]\n\nprint(\"R-squared (R2) score for Training Set:\", r2_train)\nprint(\"Mean Absolute Error (MAE) for Training Set:\", mae_train)\nprint(\"Mean Squared Error (MSE) for Training Set:\", mse_train)\nprint(\"Root Mean Squared Error (RMSE) for Training Set:\", rmse_train)\nprint(\"Mean Absolute Percentage Error (MAPE) for Training Set:\", mape_train)\nprint(\"Median Absolute Error for Training Set:\", medae_train)\nprint(\"Max Error for Training Set:\", max_err_train)\nprint(\"Correlation between predicted and real values for Training Set:\", corr_train)\n\nprint(\"#\"*100)\n#------------------------------------------------------------------------\n\n# R-squared (R2) score\nr2 = r2_score(y_test_log, y_pred)\n\n# Mean Absolute Error (MAE)\nmae = mean_absolute_error(y_test_log, y_pred)\n\n# Mean Squared Error (MSE)\nmse = mean_squared_error(y_test_log, y_pred)\n\n# Root Mean Squared Error (RMSE)\nrmse = root_mean_squared_error(y_test_log, y_pred)\n\n# Mean Absolute Percentage Error (MAPE)\n# Avoiding division by zero\ny_test_values = y_test_log.to_numpy()\ny_test_values = y_test_values.reshape(y_test_values.shape[0])\nmape = np.mean(np.abs((y_test_values - y_pred) / np.where(y_test_values == 0, 1, y_test_values))) * 100\n\n# Median Absolute Error\nmedae = median_absolute_error(y_test_log, y_pred)\n\n# Max Error\nmax_err = max_error(y_test_log, y_pred)\n\n# Correlation between predicted and real values\ncorr = np.corrcoef(y_test_values, y_pred)[0, 1]\n\nprint(\"R-squared (R2) score for Test Set:\", r2)\nprint(\"Mean Absolute Error (MAE) for Test Set:\", mae)\nprint(\"Mean Squared Error (MSE) for Test Set:\", mse)\nprint(\"Root Mean Squared Error (RMSE) for Test Set:\", rmse)\nprint(\"Mean Absolute Percentage Error (MAPE) for Test Set:\", mape)\nprint(\"Median Absolute Error for Test Set:\", medae)\nprint(\"Max Error for Test Set:\", max_err)\nprint(\"Correlation between predicted and real values for Test Set:\", corr)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T15:01:15.983354Z","iopub.execute_input":"2024-06-06T15:01:15.983754Z","iopub.status.idle":"2024-06-06T15:01:16.045344Z","shell.execute_reply.started":"2024-06-06T15:01:15.983723Z","shell.execute_reply":"2024-06-06T15:01:16.043284Z"},"trusted":true},"execution_count":24,"outputs":[{"name":"stdout","text":"R-squared (R2) score for Training Set: 0.8618489173186046\nMean Absolute Error (MAE) for Training Set: 0.10921548324521889\nMean Squared Error (MSE) for Training Set: 0.021176547045315332\nRoot Mean Squared Error (RMSE) for Training Set: 0.14552163772207669\nMean Absolute Percentage Error (MAPE) for Training Set: 0.9127642297246296\nMedian Absolute Error for Training Set: 0.08630380585047881\nMax Error for Training Set: 0.8715473634819748\nCorrelation between predicted and real values for Training Set: 0.928358183740847\n####################################################################################################\nR-squared (R2) score for Test Set: 0.4319263714997683\nMean Absolute Error (MAE) for Test Set: 0.23765278635894124\nMean Squared Error (MSE) for Test Set: 0.08771173735142324\nRoot Mean Squared Error (RMSE) for Test Set: 0.29616167434599505\nMean Absolute Percentage Error (MAPE) for Test Set: 1.9969368341096212\nMedian Absolute Error for Test Set: 0.21146968468571625\nMax Error for Test Set: 1.231651351203304\nCorrelation between predicted and real values for Test Set: 0.8131742479771871\n","output_type":"stream"}]},{"cell_type":"markdown","source":"### 5.Ckeck Feature Importances  \nTo identify features that have a noteworthy effect on the target field in the model, we can assess the feature importances.","metadata":{}},{"cell_type":"code","source":"best_reg_tree.feature_importances_\nbest_reg_tree.feature_names_in_","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:44.668098Z","iopub.execute_input":"2024-06-06T14:48:44.668431Z","iopub.status.idle":"2024-06-06T14:48:44.675353Z","shell.execute_reply.started":"2024-06-06T14:48:44.668404Z","shell.execute_reply":"2024-06-06T14:48:44.674295Z"},"trusted":true},"execution_count":11,"outputs":[{"execution_count":11,"output_type":"execute_result","data":{"text/plain":"array(['num__LotFrontage', 'num__LotArea', 'num__OverallQual',\n       'num__OverallCond', 'num__BsmtUnfSF', 'num__TotalBsmtSF',\n       'num__1stFlrSF', 'num__GrLivArea', 'num__BsmtFullBath',\n       'num__HalfBath', 'num__TotRmsAbvGrd', 'num__Fireplaces',\n       'num__GarageCars', 'num__MoSold', 'num__AgeBuilt',\n       'num__AgeRemodAdd', 'num__AgeGarageBlt', 'num__AgeSold',\n       'nom__MSZoning_RM', 'nom__LotShape_Reg',\n       'nom__LandContour_Not Lvl', 'nom__LandSlope_Not Gtl',\n       'nom__Neighborhood_CollgCr', 'nom__Neighborhood_Sawyer',\n       'nom__RoofStyle_Not Gable', 'nom__Exterior2nd_Stucco',\n       'nom__GarageType_Attchd', 'nom__GarageType_Basment',\n       'nom__Fence_non-existent', 'nom__SaleCondition_Not Normal',\n       'ord__MSSubClass', 'ord__ExterQual', 'ord__BsmtQual',\n       'ord__BsmtExposure', 'ord__BsmtFinType1', 'ord__BsmtFinSF1',\n       'ord__BsmtFinType2', 'ord__KitchenQual', 'ord__FireplaceQu',\n       'ord__GarageArea', 'ord__GarageCond', 'ord__OpenPorchSF'],\n      dtype=object)"},"metadata":{}}]},{"cell_type":"code","source":"import numpy as np\nimport matplotlib.pyplot as plt\n\n# Extract feature importances and feature names\nimportances = best_reg_tree.feature_importances_\nfeature_names = best_reg_tree.feature_names_in_\n\n# Filter out features with non-zero importances\nnon_zero_indices = np.where(importances > 0)[0]\nnon_zero_importances = importances[non_zero_indices]\nnon_zero_feature_names = feature_names[non_zero_indices]\n\n# Sort feature importances in descending order\nindices = np.argsort(non_zero_importances)\n\n# Plot\nplt.figure(figsize=(8, 4))\nplt.title(\"Feature Importances\")\nplt.barh(range(len(non_zero_feature_names)), non_zero_importances[indices], color=\"skyblue\", align=\"center\")\nplt.yticks(range(len(non_zero_feature_names)), non_zero_feature_names[indices])\nplt.xlabel(\"Importance\")\nplt.ylabel(\"Features\")\nplt.tight_layout()\nplt.show()\n","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:44.677018Z","iopub.execute_input":"2024-06-06T14:48:44.677478Z","iopub.status.idle":"2024-06-06T14:48:45.119371Z","shell.execute_reply.started":"2024-06-06T14:48:44.677439Z","shell.execute_reply":"2024-06-06T14:48:45.118249Z"},"trusted":true},"execution_count":12,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 800x400 with 1 Axes>","image/png":"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"},"metadata":{}}]},{"cell_type":"markdown","source":"### 6.Visualize the Regression Tree and If-Then Rules\nTo comprehend the structure of the model, we can utilize the Regression tree plot or represent its logic through if-then rules in textual format for **concept description** purposes.","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import plot_tree\nimport matplotlib.pyplot as plt\n\n# Assuming clf is your decision tree regressor\nplt.figure(figsize=(20,10))  # Adjust the figure size as needed\nplot_tree(best_reg_tree, max_depth=None, feature_names=best_reg_tree.feature_names_in_,\n          class_names=None, label='all',\n          filled=False, impurity=True, node_ids=False, proportion=False, rounded=False, precision=3,\n          ax=None, fontsize=None)\nplt.show()","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:45.120831Z","iopub.execute_input":"2024-06-06T14:48:45.121267Z","iopub.status.idle":"2024-06-06T14:48:47.946517Z","shell.execute_reply.started":"2024-06-06T14:48:45.121210Z","shell.execute_reply":"2024-06-06T14:48:47.945245Z"},"trusted":true},"execution_count":13,"outputs":[{"output_type":"display_data","data":{"text/plain":"<Figure size 2000x1000 with 1 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"},"metadata":{}}]},{"cell_type":"code","source":"from sklearn.tree import export_text\n\ntree_rules = export_text(best_reg_tree,feature_names=list(best_reg_tree.feature_names_in_),\n                        spacing=3,\n                        decimals=2,\n                        show_weights=True)\n\nprint(tree_rules)","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:47.947990Z","iopub.execute_input":"2024-06-06T14:48:47.948436Z","iopub.status.idle":"2024-06-06T14:48:47.958595Z","shell.execute_reply.started":"2024-06-06T14:48:47.948389Z","shell.execute_reply":"2024-06-06T14:48:47.957446Z"},"trusted":true},"execution_count":14,"outputs":[{"name":"stdout","text":"|--- num__OverallQual <= 0.27\n|   |--- num__GrLivArea <= -0.14\n|   |   |--- num__AgeBuilt <= 0.79\n|   |   |   |--- num__1stFlrSF <= -0.43\n|   |   |   |   |--- num__TotalBsmtSF <= -1.01\n|   |   |   |   |   |--- value: [11.48]\n|   |   |   |   |--- num__TotalBsmtSF >  -1.01\n|   |   |   |   |   |--- ord__BsmtFinType1 <= 0.44\n|   |   |   |   |   |   |--- value: [11.67]\n|   |   |   |   |   |--- ord__BsmtFinType1 >  0.44\n|   |   |   |   |   |   |--- value: [11.80]\n|   |   |   |--- num__1stFlrSF >  -0.43\n|   |   |   |   |--- num__OverallQual <= -0.45\n|   |   |   |   |   |--- ord__BsmtFinSF1 <= -0.00\n|   |   |   |   |   |   |--- value: [11.73]\n|   |   |   |   |   |--- ord__BsmtFinSF1 >  -0.00\n|   |   |   |   |   |   |--- value: [11.85]\n|   |   |   |   |--- num__OverallQual >  -0.45\n|   |   |   |   |   |--- num__1stFlrSF <= 0.10\n|   |   |   |   |   |   |--- value: [11.90]\n|   |   |   |   |   |--- num__1stFlrSF >  0.10\n|   |   |   |   |   |   |--- value: [12.01]\n|   |   |--- num__AgeBuilt >  0.79\n|   |   |   |--- num__GrLivArea <= -0.89\n|   |   |   |   |--- num__OverallQual <= -1.18\n|   |   |   |   |   |--- value: [11.28]\n|   |   |   |   |--- num__OverallQual >  -1.18\n|   |   |   |   |   |--- num__OverallCond <= 0.84\n|   |   |   |   |   |   |--- value: [11.44]\n|   |   |   |   |   |--- num__OverallCond >  0.84\n|   |   |   |   |   |   |--- value: [11.60]\n|   |   |   |--- num__GrLivArea >  -0.89\n|   |   |   |   |--- ord__FireplaceQu <= 0.17\n|   |   |   |   |   |--- num__BsmtUnfSF <= 0.13\n|   |   |   |   |   |   |--- value: [11.69]\n|   |   |   |   |   |--- num__BsmtUnfSF >  0.13\n|   |   |   |   |   |   |--- value: [11.55]\n|   |   |   |   |--- ord__FireplaceQu >  0.17\n|   |   |   |   |   |--- value: [11.76]\n|   |--- num__GrLivArea >  -0.14\n|   |   |--- nom__LotShape_Reg <= -0.25\n|   |   |   |--- num__GrLivArea <= 0.59\n|   |   |   |   |--- ord__BsmtQual <= 0.10\n|   |   |   |   |   |--- num__AgeRemodAdd <= 0.85\n|   |   |   |   |   |   |--- value: [12.03]\n|   |   |   |   |   |--- num__AgeRemodAdd >  0.85\n|   |   |   |   |   |   |--- value: [11.89]\n|   |   |   |   |--- ord__BsmtQual >  0.10\n|   |   |   |   |   |--- num__LotFrontage <= 0.39\n|   |   |   |   |   |   |--- value: [12.10]\n|   |   |   |   |   |--- num__LotFrontage >  0.39\n|   |   |   |   |   |   |--- value: [12.16]\n|   |   |   |--- num__GrLivArea >  0.59\n|   |   |   |   |--- ord__BsmtFinSF1 <= -0.00\n|   |   |   |   |   |--- value: [12.17]\n|   |   |   |   |--- ord__BsmtFinSF1 >  -0.00\n|   |   |   |   |   |--- value: [12.32]\n|   |   |--- nom__LotShape_Reg >  -0.25\n|   |   |   |--- ord__BsmtFinType1 <= 0.44\n|   |   |   |   |--- num__HalfBath <= 0.23\n|   |   |   |   |   |--- ord__BsmtFinType1 <= -0.51\n|   |   |   |   |   |   |--- value: [11.72]\n|   |   |   |   |   |--- ord__BsmtFinType1 >  -0.51\n|   |   |   |   |   |   |--- value: [11.90]\n|   |   |   |   |--- num__HalfBath >  0.23\n|   |   |   |   |   |--- num__AgeGarageBlt <= 0.83\n|   |   |   |   |   |   |--- value: [12.03]\n|   |   |   |   |   |--- num__AgeGarageBlt >  0.83\n|   |   |   |   |   |   |--- value: [11.89]\n|   |   |   |--- ord__BsmtFinType1 >  0.44\n|   |   |   |   |--- ord__KitchenQual <= 0.09\n|   |   |   |   |   |--- value: [12.01]\n|   |   |   |   |--- ord__KitchenQual >  0.09\n|   |   |   |   |   |--- value: [12.18]\n|--- num__OverallQual >  0.27\n|   |--- num__OverallQual <= 1.73\n|   |   |--- num__GrLivArea <= 0.90\n|   |   |   |--- num__TotalBsmtSF <= 0.94\n|   |   |   |   |--- nom__MSZoning_RM <= 1.04\n|   |   |   |   |   |--- num__1stFlrSF <= -1.18\n|   |   |   |   |   |   |--- value: [12.04]\n|   |   |   |   |   |--- num__1stFlrSF >  -1.18\n|   |   |   |   |   |   |--- value: [12.19]\n|   |   |   |   |--- nom__MSZoning_RM >  1.04\n|   |   |   |   |   |--- value: [11.89]\n|   |   |   |--- num__TotalBsmtSF >  0.94\n|   |   |   |   |--- num__BsmtFullBath <= 0.11\n|   |   |   |   |   |--- num__LotArea <= 0.20\n|   |   |   |   |   |   |--- value: [12.30]\n|   |   |   |   |   |--- num__LotArea >  0.20\n|   |   |   |   |   |   |--- value: [12.40]\n|   |   |   |   |--- num__BsmtFullBath >  0.11\n|   |   |   |   |   |--- num__GrLivArea <= 0.37\n|   |   |   |   |   |   |--- value: [12.41]\n|   |   |   |   |   |--- num__GrLivArea >  0.37\n|   |   |   |   |   |   |--- value: [12.53]\n|   |   |--- num__GrLivArea >  0.90\n|   |   |   |--- num__OverallQual <= 1.00\n|   |   |   |   |--- num__TotalBsmtSF <= 0.33\n|   |   |   |   |   |--- num__AgeBuilt <= -0.22\n|   |   |   |   |   |   |--- value: [12.38]\n|   |   |   |   |   |--- num__AgeBuilt >  -0.22\n|   |   |   |   |   |   |--- value: [12.26]\n|   |   |   |   |--- num__TotalBsmtSF >  0.33\n|   |   |   |   |   |--- value: [12.53]\n|   |   |   |--- num__OverallQual >  1.00\n|   |   |   |   |--- num__1stFlrSF <= 0.88\n|   |   |   |   |   |--- num__GrLivArea <= 1.51\n|   |   |   |   |   |   |--- value: [12.47]\n|   |   |   |   |   |--- num__GrLivArea >  1.51\n|   |   |   |   |   |   |--- value: [12.64]\n|   |   |   |   |--- num__1stFlrSF >  0.88\n|   |   |   |   |   |--- value: [12.75]\n|   |--- num__OverallQual >  1.73\n|   |   |--- num__GrLivArea <= 1.41\n|   |   |   |--- value: [12.75]\n|   |   |--- num__GrLivArea >  1.41\n|   |   |   |--- value: [13.07]\n\n","output_type":"stream"}]},{"cell_type":"markdown","source":"#### Function: Extract Rules from Regression Tree Model","metadata":{}},{"cell_type":"code","source":"from sklearn.tree import _tree\n\ndef get_rules(tree, feature_names, task):  # task='reg' or 'class'\n    tree_ = tree.tree_\n    if task == 'class':\n        class_name = tree.classes_\n    feature_name = [\n        feature_names[i] if i != _tree.TREE_UNDEFINED else \"undefined!\"\n        for i in tree_.feature\n    ]\n\n    paths = []\n    path = []\n    \n    def recurse(node, path, paths):\n        \n        if tree_.feature[node] != _tree.TREE_UNDEFINED:\n            name = feature_name[node]\n            threshold = tree_.threshold[node]\n            p1, p2 = list(path), list(path)\n            p1 += [f\"({name} <= {np.round(threshold, 3)})\"]\n            recurse(tree_.children_left[node], p1, paths)\n            p2 += [f\"({name} > {np.round(threshold, 3)})\"]\n            recurse(tree_.children_right[node], p2, paths)\n        else:\n            path += [(tree_.value[node], tree_.n_node_samples[node])]\n            paths += [path]\n            \n    recurse(0, path, paths)\n\n    # sort by samples count\n    samples_count = [p[-1][1] for p in paths]\n    ii = list(np.argsort(samples_count))\n    paths = [paths[i] for i in reversed(ii)]\n    \n    antecedents = []\n    consequents = []\n    \n    for path in paths:\n        antecedent = \"\"\n        consequent = \"\"\n        \n        for p in path[:-1]:\n            if antecedent != \"\":\n                antecedent += \" and \"\n            antecedent += str(p)\n\n        if task == 'reg':\n            consequent += str(np.round(path[-1][0][0][0], 3))\n        elif task == 'class':\n            classes = path[-1][0][0]\n            l = np.argmax(classes)\n            \n            # Get class name for antecedent\n            consequent += class_name[l]\n\n        antecedents.append(antecedent)\n        consequents.append(consequent)\n    \n    rules = pd.DataFrame({'Antecedent':antecedents, 'Consequent':consequents})\n        \n    return rules","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:47.960163Z","iopub.execute_input":"2024-06-06T14:48:47.960599Z","iopub.status.idle":"2024-06-06T14:48:47.977910Z","shell.execute_reply.started":"2024-06-06T14:48:47.960564Z","shell.execute_reply":"2024-06-06T14:48:47.976787Z"},"trusted":true},"execution_count":15,"outputs":[]},{"cell_type":"markdown","source":"#### Function: Create Evaluation Report for the Extracted Rules","metadata":{}},{"cell_type":"code","source":"def calculate_rule_metrics(rules_df, input_data, target_variable, task):\n\n    import re\n\n    # Add columns for metrics\n    rules_df['Sample'] = None\n    rules_df['Support'] = None\n    rules_df['Confidence'] = None\n    rules_df['Lift'] = None\n    \n    # Calculate metrics for each rule\n    for i, row in rules_df.iterrows():\n        antecedent = row['Antecedent']\n        consequent = row['Consequent']\n        \n        def filter_by_antecedent(data, antecedent):\n\n            conditions = antecedent.split(\" and \")\n            filtered_data = data.copy()  # Avoid modifying original data\n            pattern = r\"(?P<col_name>\\w+)\\s*(?P<operator>[<>=]+)\\s*(?P<threshold>[-.\\d]+)\"\n\n            for condition in conditions:\n                condition = condition.strip('()')\n                match = re.match(pattern, condition)\n                if not match:\n                    raise ValueError(f\"Invalid condition format: {condition}\")\n\n                col_name, operator, threshold = match.groups()\n                # Access the appropriate comparison function directly using operator\n                comparison_func = {\n                    \"<\": pd.Series.__lt__,\n                    \">\": pd.Series.__gt__,\n                    \"<=\": pd.Series.__le__,\n                    \">=\": pd.Series.__ge__,\n                    \"==\": pd.Series.__eq__,\n                }[operator]\n\n                filtered_data = filtered_data[comparison_func(filtered_data[col_name], float(threshold))]\n\n            return filtered_data\n\n\n\n        # Filter train_data based on antecedent conditions (boolean mask)\n        filtered_data = filter_by_antecedent(input_data, antecedent)\n        index = filtered_data.index.tolist()\n\n        # Calculate support (proportion of samples satisfying the rule)\n        rule_support = len(filtered_data) / len(input_data)\n        rules_df.at[i, 'Sample'] = len(filtered_data)\n        rules_df.at[i, 'Support'] = rule_support\n\n        # Calculate confidence (conditional probability of consequent given antecedent) and lift index\n        if task == 'class':\n            \n            if len(filtered_data) == 0:\n                confidence = None\n                lift = None\n            else:\n                class_probabilities = target_variable.value_counts(normalize=True)\n                if target_variable.index.name:\n                    class_counts = target_variable.loc[index].value_counts()\n                else:\n                    class_counts = target_variable[index].value_counts()\n                if consequent in class_counts:\n                    correct_predictions = class_counts[consequent]\n                else:\n                    correct_predictions = 0\n                confidence = (correct_predictions / len(filtered_data)) * 100\n                lift = (confidence / 100) / class_probabilities[consequent]       \n\n        elif task == 'reg':\n\n            # Confidence and Lift calculation for regression is not typically used\n            confidence = None\n            lift = None\n\n        rules_df.at[i, 'Confidence'] = confidence\n        rules_df.at[i, 'Lift'] = lift\n        \n    return rules_df\n","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:47.979043Z","iopub.execute_input":"2024-06-06T14:48:47.979375Z","iopub.status.idle":"2024-06-06T14:48:47.996510Z","shell.execute_reply.started":"2024-06-06T14:48:47.979349Z","shell.execute_reply":"2024-06-06T14:48:47.995382Z"},"trusted":true},"execution_count":16,"outputs":[]},{"cell_type":"markdown","source":"#### Get Rules from House Price Regression Tree","metadata":{}},{"cell_type":"code","source":"feature_names = X_train.columns.tolist()\n\n# Set the maximum column width\npd.set_option('display.max_colwidth', None)\n\nrules = get_rules(best_reg_tree, feature_names, task='reg')\nrules","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:47.997893Z","iopub.execute_input":"2024-06-06T14:48:47.998258Z","iopub.status.idle":"2024-06-06T14:48:48.029411Z","shell.execute_reply.started":"2024-06-06T14:48:47.998206Z","shell.execute_reply":"2024-06-06T14:48:48.028303Z"},"trusted":true},"execution_count":17,"outputs":[{"execution_count":17,"output_type":"execute_result","data":{"text/plain":"                                                                                                                                                                                    Antecedent  \\\n0         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF > -1.18)   \n1          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 > -0.003)   \n2   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 <= -0.509)   \n3        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 <= 0.443)   \n4              (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF > 0.098)   \n5         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 > 0.443)   \n6             (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF <= 0.098)   \n7                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea <= 1.413)   \n8         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 <= -0.003)   \n9        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt <= -0.223)   \n10                                      (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual <= -1.183)   \n11       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF <= -1.18)   \n12           (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF > 0.132)   \n13        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea > 0.368)   \n14        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond > 0.841)   \n15          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF <= 0.132)   \n16                                       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF <= -1.006)   \n17                                  (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual <= 0.087)   \n18            (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea <= 1.51)   \n19    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt <= 0.825)   \n20                                      (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF > 0.334)   \n21        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea <= 0.203)   \n22       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond <= 0.841)   \n23                                   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual > 0.087)   \n24   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 > -0.509)   \n25                                     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 > -0.003)   \n26         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea > 0.203)   \n27        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt > -0.223)   \n28        (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage > 0.391)   \n29       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage <= 0.391)   \n30       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd > 0.852)   \n31             (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea > 1.51)   \n32                                          (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF > 0.875)   \n33     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt > 0.825)   \n34       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea <= 0.368)   \n35                                     (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM > 1.044)   \n36                                         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu > 0.165)   \n37      (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd <= 0.852)   \n38                                    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 <= -0.003)   \n39                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea > 1.413)   \n\n   Consequent  \n0      12.192  \n1      11.855  \n2      11.719  \n3      11.674  \n4      12.014  \n5      11.803  \n6      11.904  \n7      12.747  \n8      11.725  \n9      12.382  \n10     11.276  \n11      12.04  \n12      11.55  \n13     12.525  \n14     11.603  \n15     11.692  \n16     11.478  \n17     12.005  \n18     12.473  \n19     12.027  \n20     12.533  \n21     12.295  \n22     11.438  \n23     12.182  \n24     11.902  \n25     12.316  \n26     12.402  \n27     12.263  \n28     12.159  \n29     12.098  \n30     11.887  \n31     12.639  \n32     12.747  \n33     11.888  \n34     12.405  \n35     11.894  \n36     11.762  \n37     12.031  \n38     12.174  \n39     13.067  ","text/html":"<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>Antecedent</th>\n      <th>Consequent</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &gt; -1.18)</td>\n      <td>12.192</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>11.855</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &lt;= -0.509)</td>\n      <td>11.719</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &lt;= 0.443)</td>\n      <td>11.674</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &gt; 0.098)</td>\n      <td>12.014</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &gt; 0.443)</td>\n      <td>11.803</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &lt;= 0.098)</td>\n      <td>11.904</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &lt;= 1.413)</td>\n      <td>12.747</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>11.725</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &lt;= -0.223)</td>\n      <td>12.382</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &lt;= -1.183)</td>\n      <td>11.276</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &lt;= -1.18)</td>\n      <td>12.04</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &gt; 0.132)</td>\n      <td>11.55</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &gt; 0.368)</td>\n      <td>12.525</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &gt; 0.841)</td>\n      <td>11.603</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &lt;= 0.132)</td>\n      <td>11.692</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &lt;= -1.006)</td>\n      <td>11.478</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &lt;= 0.087)</td>\n      <td>12.005</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &lt;= 1.51)</td>\n      <td>12.473</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &lt;= 0.825)</td>\n      <td>12.027</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &gt; 0.334)</td>\n      <td>12.533</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &lt;= 0.203)</td>\n      <td>12.295</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &lt;= 0.841)</td>\n      <td>11.438</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &gt; 0.087)</td>\n      <td>12.182</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &gt; -0.509)</td>\n      <td>11.902</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>12.316</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &gt; 0.203)</td>\n      <td>12.402</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &gt; -0.223)</td>\n      <td>12.263</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &gt; 0.391)</td>\n      <td>12.159</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &lt;= 0.391)</td>\n      <td>12.098</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &gt; 0.852)</td>\n      <td>11.887</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &gt; 1.51)</td>\n      <td>12.639</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &gt; 0.875)</td>\n      <td>12.747</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &gt; 0.825)</td>\n      <td>11.888</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &lt;= 0.368)</td>\n      <td>12.405</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &gt; 1.044)</td>\n      <td>11.894</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &gt; 0.165)</td>\n      <td>11.762</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &lt;= 0.852)</td>\n      <td>12.031</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>12.174</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &gt; 1.413)</td>\n      <td>13.067</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Report Table for the Extracted Rules on Train Dataset","metadata":{}},{"cell_type":"code","source":"import pandas as pd\n\ncalculate_rule_metrics(rules, X_train, y_train_log, task='reg')","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:48.030580Z","iopub.execute_input":"2024-06-06T14:48:48.030895Z","iopub.status.idle":"2024-06-06T14:48:48.150996Z","shell.execute_reply.started":"2024-06-06T14:48:48.030870Z","shell.execute_reply":"2024-06-06T14:48:48.150015Z"},"trusted":true},"execution_count":18,"outputs":[{"execution_count":18,"output_type":"execute_result","data":{"text/plain":"                                                                                                                                                                                    Antecedent  \\\n0         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF > -1.18)   \n1          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 > -0.003)   \n2   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 <= -0.509)   \n3        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 <= 0.443)   \n4              (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF > 0.098)   \n5         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 > 0.443)   \n6             (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF <= 0.098)   \n7                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea <= 1.413)   \n8         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 <= -0.003)   \n9        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt <= -0.223)   \n10                                      (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual <= -1.183)   \n11       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF <= -1.18)   \n12           (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF > 0.132)   \n13        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea > 0.368)   \n14        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond > 0.841)   \n15          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF <= 0.132)   \n16                                       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF <= -1.006)   \n17                                  (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual <= 0.087)   \n18            (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea <= 1.51)   \n19    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt <= 0.825)   \n20                                      (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF > 0.334)   \n21        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea <= 0.203)   \n22       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond <= 0.841)   \n23                                   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual > 0.087)   \n24   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 > -0.509)   \n25                                     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 > -0.003)   \n26         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea > 0.203)   \n27        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt > -0.223)   \n28        (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage > 0.391)   \n29       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage <= 0.391)   \n30       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd > 0.852)   \n31             (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea > 1.51)   \n32                                          (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF > 0.875)   \n33     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt > 0.825)   \n34       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea <= 0.368)   \n35                                     (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM > 1.044)   \n36                                         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu > 0.165)   \n37      (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd <= 0.852)   \n38                                    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 <= -0.003)   \n39                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea > 1.413)   \n\n   Consequent Sample   Support Confidence  Lift  \n0      12.192    116  0.115884       None  None  \n1      11.855     66  0.065934       None  None  \n2      11.719     46  0.045954       None  None  \n3      11.674     46  0.045954       None  None  \n4      12.014     36  0.035964       None  None  \n5      11.803     34  0.033966       None  None  \n6      11.904     27  0.026973       None  None  \n7      12.747     26  0.025974       None  None  \n8      11.725     26  0.025974       None  None  \n9      12.382     26  0.025974       None  None  \n10     11.276     26  0.025974       None  None  \n11      12.04     26  0.025974       None  None  \n12      11.55     25  0.024975       None  None  \n13     12.525     23  0.022977       None  None  \n14     11.603     23  0.022977       None  None  \n15     11.692     21  0.020979       None  None  \n16     11.478     20   0.01998       None  None  \n17     12.005     20   0.01998       None  None  \n18     12.473     20   0.01998       None  None  \n19     12.027     19  0.018981       None  None  \n20     12.533     19  0.018981       None  None  \n21     12.295     19  0.018981       None  None  \n22     11.438     19  0.018981       None  None  \n23     12.182     18  0.017982       None  None  \n24     11.902     18  0.017982       None  None  \n25     12.316     18  0.017982       None  None  \n26     12.402     18  0.017982       None  None  \n27     12.263     16  0.015984       None  None  \n28     12.159     16  0.015984       None  None  \n29     12.098     16  0.015984       None  None  \n30     11.887     16  0.015984       None  None  \n31     12.639     16  0.015984       None  None  \n32     12.747     15  0.014985       None  None  \n33     11.888     15  0.014985       None  None  \n34     12.405     15  0.014985       None  None  \n35     11.894     15  0.014985       None  None  \n36     11.762     15  0.014985       None  None  \n37     12.031     15  0.014985       None  None  \n38     12.174     15  0.014985       None  None  \n39     13.067     15  0.014985       None  None  ","text/html":"<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>Antecedent</th>\n      <th>Consequent</th>\n      <th>Sample</th>\n      <th>Support</th>\n      <th>Confidence</th>\n      <th>Lift</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &gt; -1.18)</td>\n      <td>12.192</td>\n      <td>116</td>\n      <td>0.115884</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>11.855</td>\n      <td>66</td>\n      <td>0.065934</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &lt;= -0.509)</td>\n      <td>11.719</td>\n      <td>46</td>\n      <td>0.045954</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &lt;= 0.443)</td>\n      <td>11.674</td>\n      <td>46</td>\n      <td>0.045954</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &gt; 0.098)</td>\n      <td>12.014</td>\n      <td>36</td>\n      <td>0.035964</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &gt; 0.443)</td>\n      <td>11.803</td>\n      <td>34</td>\n      <td>0.033966</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &lt;= 0.098)</td>\n      <td>11.904</td>\n      <td>27</td>\n      <td>0.026973</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &lt;= 1.413)</td>\n      <td>12.747</td>\n      <td>26</td>\n      <td>0.025974</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>11.725</td>\n      <td>26</td>\n      <td>0.025974</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &lt;= -0.223)</td>\n      <td>12.382</td>\n      <td>26</td>\n      <td>0.025974</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &lt;= -1.183)</td>\n      <td>11.276</td>\n      <td>26</td>\n      <td>0.025974</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &lt;= -1.18)</td>\n      <td>12.04</td>\n      <td>26</td>\n      <td>0.025974</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &gt; 0.132)</td>\n      <td>11.55</td>\n      <td>25</td>\n      <td>0.024975</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &gt; 0.368)</td>\n      <td>12.525</td>\n      <td>23</td>\n      <td>0.022977</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &gt; 0.841)</td>\n      <td>11.603</td>\n      <td>23</td>\n      <td>0.022977</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &lt;= 0.132)</td>\n      <td>11.692</td>\n      <td>21</td>\n      <td>0.020979</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &lt;= -1.006)</td>\n      <td>11.478</td>\n      <td>20</td>\n      <td>0.01998</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &lt;= 0.087)</td>\n      <td>12.005</td>\n      <td>20</td>\n      <td>0.01998</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &lt;= 1.51)</td>\n      <td>12.473</td>\n      <td>20</td>\n      <td>0.01998</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &lt;= 0.825)</td>\n      <td>12.027</td>\n      <td>19</td>\n      <td>0.018981</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &gt; 0.334)</td>\n      <td>12.533</td>\n      <td>19</td>\n      <td>0.018981</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &lt;= 0.203)</td>\n      <td>12.295</td>\n      <td>19</td>\n      <td>0.018981</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &lt;= 0.841)</td>\n      <td>11.438</td>\n      <td>19</td>\n      <td>0.018981</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &gt; 0.087)</td>\n      <td>12.182</td>\n      <td>18</td>\n      <td>0.017982</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &gt; -0.509)</td>\n      <td>11.902</td>\n      <td>18</td>\n      <td>0.017982</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>12.316</td>\n      <td>18</td>\n      <td>0.017982</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &gt; 0.203)</td>\n      <td>12.402</td>\n      <td>18</td>\n      <td>0.017982</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &gt; -0.223)</td>\n      <td>12.263</td>\n      <td>16</td>\n      <td>0.015984</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &gt; 0.391)</td>\n      <td>12.159</td>\n      <td>16</td>\n      <td>0.015984</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &lt;= 0.391)</td>\n      <td>12.098</td>\n      <td>16</td>\n      <td>0.015984</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &gt; 0.852)</td>\n      <td>11.887</td>\n      <td>16</td>\n      <td>0.015984</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &gt; 1.51)</td>\n      <td>12.639</td>\n      <td>16</td>\n      <td>0.015984</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &gt; 0.875)</td>\n      <td>12.747</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &gt; 0.825)</td>\n      <td>11.888</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &lt;= 0.368)</td>\n      <td>12.405</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &gt; 1.044)</td>\n      <td>11.894</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &gt; 0.165)</td>\n      <td>11.762</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &lt;= 0.852)</td>\n      <td>12.031</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>12.174</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &gt; 1.413)</td>\n      <td>13.067</td>\n      <td>15</td>\n      <td>0.014985</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]},{"cell_type":"markdown","source":"#### Report Table for the Extracted Rules on Test Dataset","metadata":{}},{"cell_type":"code","source":"calculate_rule_metrics(rules, X_test, y_test_log, task='reg')","metadata":{"execution":{"iopub.status.busy":"2024-06-06T14:48:48.153018Z","iopub.execute_input":"2024-06-06T14:48:48.153429Z","iopub.status.idle":"2024-06-06T14:48:48.262314Z","shell.execute_reply.started":"2024-06-06T14:48:48.153393Z","shell.execute_reply":"2024-06-06T14:48:48.261298Z"},"trusted":true},"execution_count":19,"outputs":[{"execution_count":19,"output_type":"execute_result","data":{"text/plain":"                                                                                                                                                                                    Antecedent  \\\n0         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF > -1.18)   \n1          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 > -0.003)   \n2   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 <= -0.509)   \n3        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 <= 0.443)   \n4              (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF > 0.098)   \n5         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF > -1.006) and (ord__BsmtFinType1 > 0.443)   \n6             (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual > -0.454) and (num__1stFlrSF <= 0.098)   \n7                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea <= 1.413)   \n8         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF > -0.43) and (num__OverallQual <= -0.454) and (ord__BsmtFinSF1 <= -0.003)   \n9        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt <= -0.223)   \n10                                      (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual <= -1.183)   \n11       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM <= 1.044) and (num__1stFlrSF <= -1.18)   \n12           (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF > 0.132)   \n13        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea > 0.368)   \n14        (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond > 0.841)   \n15          (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu <= 0.165) and (num__BsmtUnfSF <= 0.132)   \n16                                       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt <= 0.788) and (num__1stFlrSF <= -0.43) and (num__TotalBsmtSF <= -1.006)   \n17                                  (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual <= 0.087)   \n18            (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea <= 1.51)   \n19    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt <= 0.825)   \n20                                      (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF > 0.334)   \n21        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea <= 0.203)   \n22       (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea <= -0.887) and (num__OverallQual > -1.183) and (num__OverallCond <= 0.841)   \n23                                   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 > 0.443) and (ord__KitchenQual > 0.087)   \n24   (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath <= 0.232) and (ord__BsmtFinType1 > -0.509)   \n25                                     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 > -0.003)   \n26         (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath <= 0.113) and (num__LotArea > 0.203)   \n27        (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual <= 1.004) and (num__TotalBsmtSF <= 0.334) and (num__AgeBuilt > -0.223)   \n28        (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage > 0.391)   \n29       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual > 0.099) and (num__LotFrontage <= 0.391)   \n30       (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd > 0.852)   \n31             (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF <= 0.875) and (num__GrLivArea > 1.51)   \n32                                          (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea > 0.901) and (num__OverallQual > 1.004) and (num__1stFlrSF > 0.875)   \n33     (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg > -0.248) and (ord__BsmtFinType1 <= 0.443) and (num__HalfBath > 0.232) and (num__AgeGarageBlt > 0.825)   \n34       (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF > 0.937) and (num__BsmtFullBath > 0.113) and (num__GrLivArea <= 0.368)   \n35                                     (num__OverallQual > 0.275) and (num__OverallQual <= 1.733) and (num__GrLivArea <= 0.901) and (num__TotalBsmtSF <= 0.937) and (nom__MSZoning_RM > 1.044)   \n36                                         (num__OverallQual <= 0.275) and (num__GrLivArea <= -0.137) and (num__AgeBuilt > 0.788) and (num__GrLivArea > -0.887) and (ord__FireplaceQu > 0.165)   \n37      (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea <= 0.594) and (ord__BsmtQual <= 0.099) and (num__AgeRemodAdd <= 0.852)   \n38                                    (num__OverallQual <= 0.275) and (num__GrLivArea > -0.137) and (nom__LotShape_Reg <= -0.248) and (num__GrLivArea > 0.594) and (ord__BsmtFinSF1 <= -0.003)   \n39                                                                                                      (num__OverallQual > 0.275) and (num__OverallQual > 1.733) and (num__GrLivArea > 1.413)   \n\n   Consequent Sample   Support Confidence  Lift  \n0      12.192      1  0.002283       None  None  \n1      11.855      3  0.006849       None  None  \n2      11.719     62  0.141553       None  None  \n3      11.674      0       0.0       None  None  \n4      12.014      1  0.002283       None  None  \n5      11.803      0       0.0       None  None  \n6      11.904      0       0.0       None  None  \n7      12.747      0       0.0       None  None  \n8      11.725      0       0.0       None  None  \n9      12.382     34  0.077626       None  None  \n10     11.276      0       0.0       None  None  \n11      12.04      0       0.0       None  None  \n12      11.55      1  0.002283       None  None  \n13     12.525      0       0.0       None  None  \n14     11.603      0       0.0       None  None  \n15     11.692      2  0.004566       None  None  \n16     11.478      0       0.0       None  None  \n17     12.005     44  0.100457       None  None  \n18     12.473      0       0.0       None  None  \n19     12.027     16   0.03653       None  None  \n20     12.533     39  0.089041       None  None  \n21     12.295      0       0.0       None  None  \n22     11.438      0       0.0       None  None  \n23     12.182     19  0.043379       None  None  \n24     11.902     39  0.089041       None  None  \n25     12.316     37  0.084475       None  None  \n26     12.402      0       0.0       None  None  \n27     12.263     16   0.03653       None  None  \n28     12.159      1  0.002283       None  None  \n29     12.098      1  0.002283       None  None  \n30     11.887      7  0.015982       None  None  \n31     12.639      0       0.0       None  None  \n32     12.747     49  0.111872       None  None  \n33     11.888     16   0.03653       None  None  \n34     12.405      0       0.0       None  None  \n35     11.894      0       0.0       None  None  \n36     11.762      1  0.002283       None  None  \n37     12.031      3  0.006849       None  None  \n38     12.174     29   0.06621       None  None  \n39     13.067     17  0.038813       None  None  ","text/html":"<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>Antecedent</th>\n      <th>Consequent</th>\n      <th>Sample</th>\n      <th>Support</th>\n      <th>Confidence</th>\n      <th>Lift</th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>0</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &gt; -1.18)</td>\n      <td>12.192</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>1</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>11.855</td>\n      <td>3</td>\n      <td>0.006849</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>2</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &lt;= -0.509)</td>\n      <td>11.719</td>\n      <td>62</td>\n      <td>0.141553</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>3</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &lt;= 0.443)</td>\n      <td>11.674</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>4</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &gt; 0.098)</td>\n      <td>12.014</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>5</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &gt; -1.006) and (ord__BsmtFinType1 &gt; 0.443)</td>\n      <td>11.803</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>6</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &gt; -0.454) and (num__1stFlrSF &lt;= 0.098)</td>\n      <td>11.904</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>7</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &lt;= 1.413)</td>\n      <td>12.747</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>8</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &gt; -0.43) and (num__OverallQual &lt;= -0.454) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>11.725</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>9</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &lt;= -0.223)</td>\n      <td>12.382</td>\n      <td>34</td>\n      <td>0.077626</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>10</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &lt;= -1.183)</td>\n      <td>11.276</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>11</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &lt;= 1.044) and (num__1stFlrSF &lt;= -1.18)</td>\n      <td>12.04</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>12</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &gt; 0.132)</td>\n      <td>11.55</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>13</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &gt; 0.368)</td>\n      <td>12.525</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>14</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &gt; 0.841)</td>\n      <td>11.603</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>15</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &lt;= 0.165) and (num__BsmtUnfSF &lt;= 0.132)</td>\n      <td>11.692</td>\n      <td>2</td>\n      <td>0.004566</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>16</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &lt;= 0.788) and (num__1stFlrSF &lt;= -0.43) and (num__TotalBsmtSF &lt;= -1.006)</td>\n      <td>11.478</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>17</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &lt;= 0.087)</td>\n      <td>12.005</td>\n      <td>44</td>\n      <td>0.100457</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>18</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &lt;= 1.51)</td>\n      <td>12.473</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>19</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &lt;= 0.825)</td>\n      <td>12.027</td>\n      <td>16</td>\n      <td>0.03653</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>20</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &gt; 0.334)</td>\n      <td>12.533</td>\n      <td>39</td>\n      <td>0.089041</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>21</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &lt;= 0.203)</td>\n      <td>12.295</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>22</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &lt;= -0.887) and (num__OverallQual &gt; -1.183) and (num__OverallCond &lt;= 0.841)</td>\n      <td>11.438</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>23</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &gt; 0.443) and (ord__KitchenQual &gt; 0.087)</td>\n      <td>12.182</td>\n      <td>19</td>\n      <td>0.043379</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>24</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &lt;= 0.232) and (ord__BsmtFinType1 &gt; -0.509)</td>\n      <td>11.902</td>\n      <td>39</td>\n      <td>0.089041</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>25</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &gt; -0.003)</td>\n      <td>12.316</td>\n      <td>37</td>\n      <td>0.084475</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>26</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &lt;= 0.113) and (num__LotArea &gt; 0.203)</td>\n      <td>12.402</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>27</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &lt;= 1.004) and (num__TotalBsmtSF &lt;= 0.334) and (num__AgeBuilt &gt; -0.223)</td>\n      <td>12.263</td>\n      <td>16</td>\n      <td>0.03653</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>28</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &gt; 0.391)</td>\n      <td>12.159</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>29</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &gt; 0.099) and (num__LotFrontage &lt;= 0.391)</td>\n      <td>12.098</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>30</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &gt; 0.852)</td>\n      <td>11.887</td>\n      <td>7</td>\n      <td>0.015982</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>31</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &lt;= 0.875) and (num__GrLivArea &gt; 1.51)</td>\n      <td>12.639</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>32</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &gt; 0.901) and (num__OverallQual &gt; 1.004) and (num__1stFlrSF &gt; 0.875)</td>\n      <td>12.747</td>\n      <td>49</td>\n      <td>0.111872</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>33</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &gt; -0.248) and (ord__BsmtFinType1 &lt;= 0.443) and (num__HalfBath &gt; 0.232) and (num__AgeGarageBlt &gt; 0.825)</td>\n      <td>11.888</td>\n      <td>16</td>\n      <td>0.03653</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>34</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &gt; 0.937) and (num__BsmtFullBath &gt; 0.113) and (num__GrLivArea &lt;= 0.368)</td>\n      <td>12.405</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>35</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &lt;= 1.733) and (num__GrLivArea &lt;= 0.901) and (num__TotalBsmtSF &lt;= 0.937) and (nom__MSZoning_RM &gt; 1.044)</td>\n      <td>11.894</td>\n      <td>0</td>\n      <td>0.0</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>36</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &lt;= -0.137) and (num__AgeBuilt &gt; 0.788) and (num__GrLivArea &gt; -0.887) and (ord__FireplaceQu &gt; 0.165)</td>\n      <td>11.762</td>\n      <td>1</td>\n      <td>0.002283</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>37</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &lt;= 0.594) and (ord__BsmtQual &lt;= 0.099) and (num__AgeRemodAdd &lt;= 0.852)</td>\n      <td>12.031</td>\n      <td>3</td>\n      <td>0.006849</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>38</th>\n      <td>(num__OverallQual &lt;= 0.275) and (num__GrLivArea &gt; -0.137) and (nom__LotShape_Reg &lt;= -0.248) and (num__GrLivArea &gt; 0.594) and (ord__BsmtFinSF1 &lt;= -0.003)</td>\n      <td>12.174</td>\n      <td>29</td>\n      <td>0.06621</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n    <tr>\n      <th>39</th>\n      <td>(num__OverallQual &gt; 0.275) and (num__OverallQual &gt; 1.733) and (num__GrLivArea &gt; 1.413)</td>\n      <td>13.067</td>\n      <td>17</td>\n      <td>0.038813</td>\n      <td>None</td>\n      <td>None</td>\n    </tr>\n  </tbody>\n</table>\n</div>"},"metadata":{}}]}]}