This example predicts the price of houses based on different factors.
Run AutoRegressor to get the best performing model with following specifications:
- Set early stopping criteria, that is, time limit to 200 sec and performance metrics R2 threshold value to 0.6.
- Exclude 'glm', 'svm', and 'knn' model from default model training list.
- Opt for verbose level 2 to get detailed logging.
- Use custom_config_file to customize some specific processes in AutoML flow.
- Load the example dataset.
>>> load_example_data("decisionforestpredict", ["housing_train", "housing_test"])>>> housing_train = DataFrame.from_table("housing_train")>>> housing_test = DataFrame.from_table("housing_test") - Generate custom config JSON file.
>>> AutoRegressor.generate_custom_config("custom_housing")Generating custom config JSON for AutoML ... Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 1 Customizing Feature Engineering Phase ... Available options for customization of feature engineering phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Missing Value Handling Index 2: Customize Bincode Encoding Index 3: Customize String Manipulation Index 4: Customize Categorical Encoding Index 5: Customize Mathematical Transformation Index 6: Customize Nonlinear Transformation Index 7: Customize Antiselect Features Index 8: Back to main menu Index 9: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in feature engineering phase: 2,4,7,8 Customizing Bincode Encoding ... Provide the following details to customize binning and coding encoding: Available binning methods with corresponding indices: Index 1: Equal-Width Index 2: Variable-Width Enter the feature or list of features for binning: bedrooms Enter the index of corresponding binning method for feature bedrooms: 2 Enter the number of bins for feature bedrooms: 2 Available value type of feature for variable binning with corresponding indices: Index 1: int Index 2: float Provide the range for bin 1 of feature bedrooms: Enter the index of corresponding value type of feature bedrooms: 1 Enter the minimum value for bin 1 of feature bedrooms: 0 Enter the maximum value for bin 1 of feature bedrooms: 2 Enter the label for bin 1 of feature bedrooms: small_house Provide the range for bin 2 of feature bedrooms: Enter the index of corresponding value type of feature bedrooms: 1 Enter the minimum value for bin 2 of feature bedrooms: 3 Enter the maximum value for bin 2 of feature bedrooms: 6 Enter the label for bin 2 of feature bedrooms: big_house Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 0 Customization of bincode encoding has been completed successfully. Customizing Categorical Encoding ... Provide the following details to customize categorical encoding: Available categorical encoding methods with corresponding indices: Index 1: OneHotEncoding Index 2: OrdinalEncoding Index 3: TargetEncoding Enter the list of corresponding index categorical encoding methods you want to use: 2,3 Enter the feature or list of features for OrdinalEncoding: homestyle Enter the feature or list of features for TargetEncoding: prefarea Available target encoding methods with corresponding indices: Index 1: CBM_BETA Index 2: CBM_DIRICHLET Index 3: CBM_GAUSSIAN_INVERSE_GAMMA Enter the index of target encoding method for feature prefarea: 3 Enter the response column for target encoding method for feature prefarea: price Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 0 Customization of categorical encoding has been completed successfully. Customizing Antiselect Features ... Enter the feature or list of features for antiselect: sn Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 0 Customization of antiselect features has been completed successfully. Customization of feature engineering phase has been completed successfully. Available main options for customization with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Feature Engineering Phase Index 2: Customize Data Preparation Phase Index 3: Customize Model Training Phase Index 4: Generate custom json and exit -------------------------------------------------------------------------------- Enter the index you want to customize: 2 Customizing Data Preparation Phase ... Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 1,2 Customizing Data Imbalance Handling ... Available data sampling methods with corresponding indices: Index 1: SMOTE Index 2: NearMiss Enter the corresponding index data imbalance handling method: 1 Customization of data imbalance handling has been completed successfully. Customizing Outlier Handling ... Available outlier detection methods with corresponding indices: Index 1: percentile Index 2: tukey Index 3: carling Enter the corresponding index oulier handling method: 1 Enter the lower percentile value for outlier handling: 0.15 Enter the upper percentile value for outlier handling: 0.85 Enter the feature or list of features for outlier handling: bathrms Available outlier replacement methods with corresponding indices: Index 1: delete Index 2: median Index 3: Any Numeric Value Enter the index of corresponding replacement method for feature bathrms: 1 Available options for generic arguments: Index 0: Default Index 1: volatile Index 2: persist Enter the indices for generic arguments : 0 Customization of outlier handling has been completed successfully. Available options for customization of data preparation phase with corresponding indices: -------------------------------------------------------------------------------- Index 1: Customize Data Imbalance Handling Index 2: Customize Outlier Handling Index 3: Customize Feature Scaling Index 4: Back to main menu Index 5: Generate custom json and exit -------------------------------------------------------------------------------- Enter the list of indices you want to customize in data preparation phase: 5 Customization of data preparation phase has been completed successfully. Process of generating custom config file for AutoML has been completed successfully. 'custom_housing.json' file is generated successfully under the current working directory.
- Create an AutoRegressor instance.
>>> aml = AutoRegressor(exclude=['glm','svm','knn'], verbose=2, max_runtime_secs=200, stopping_metric='R2', stopping_tolerance=0.6, custom_config_file='custom_housing.json') - Fit the data.
>>> aml.fit(housing_train,housing_train.price)
2025-11-04 01:22:22,305 | INFO | Received below input for customization : { "BincodeIndicator": true, "BincodeParam": { "bedrooms": { "Type": "Variable-Width", "NumOfBins": 2, "Bin_1": { "min_value": 0, "max_value": 2, "label": "small_house" }, "Bin_2": { "min_value": 3, "max_value": 6, "label": "big_house" } } }, "CategoricalEncodingIndicator": true, "CategoricalEncodingParam": { "OrdinalEncodingIndicator": true, "OrdinalEncodingList": [ "homestyle" ], "TargetEncodingIndicator": true, "TargetEncodingList": { "prefarea": { "encoder_method": "CBM_GAUSSIAN_INVERSE_GAMMA", "response_column": "price" } } }, "AntiselectIndicator": true, "AntiselectParam": { "excluded_columns": [ "sn" ] }, "DataImbalanceIndicator": true, "DataImbalanceMethod": "SMOTE", "OutlierFilterIndicator": true, "OutlierFilterMethod": "percentile", "OutlierLowerPercentile": 0.15, "OutlierUpperPercentile": 0.85, "OutlierFilterParam": { "bathrms": { "replacement_value": "delete" } } } 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 01:22:22,306 | INFO | Feature Exploration started 2025-11-04 01:22:22,306 | INFO | Data Overview: 2025-11-04 01:22:22,327 | INFO | Total Rows in the data: 492 2025-11-04 01:22:22,349 | INFO | Total Columns in the data: 14 2025-11-04 01:22:22,944 | INFO | Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage 0 bedrooms INTEGER 492 0 NaN 0.0 492.0 0.0 0.0 100.0 1 fullbase VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 2 gashw VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 3 airco VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 4 homestyle VARCHAR(20) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 5 sn INTEGER 492 0 NaN 0.0 492.0 0.0 0.0 100.0 6 driveway VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 7 bathrms INTEGER 492 0 NaN 0.0 492.0 0.0 0.0 100.0 8 recroom VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 9 prefarea VARCHAR(10) CHARACTER SET LATIN 492 0 0.0 NaN NaN NaN 0.0 100.0 10 garagepl INTEGER 492 0 NaN 270.0 222.0 0.0 0.0 100.0 11 stories INTEGER 492 0 NaN 0.0 492.0 0.0 0.0 100.0 12 lotsize FLOAT 492 0 NaN 0.0 492.0 0.0 0.0 100.0 13 price FLOAT 492 0 NaN 0.0 492.0 0.0 0.0 100.0 2025-11-04 01:22:24,406 | INFO | Statistics of Data: ATTRIBUTE StatName StatValue 0 garagepl MAXIMUM 3.0 1 bedrooms MINIMUM 1.0 2 bedrooms MAXIMUM 6.0 3 sn COUNT 492.0 4 sn MAXIMUM 546.0 5 bathrms COUNT 492.0 6 bathrms MINIMUM 1.0 7 bathrms MAXIMUM 4.0 8 sn MINIMUM 1.0 9 bedrooms COUNT 492.0 2025-11-04 01:22:24,555 | INFO | Categorical Columns with their Distinct values: ColumnName DistinctValueCount driveway 2 recroom 2 fullbase 2 gashw 2 airco 2 prefarea 2 homestyle 3 2025-11-04 01:22:26,898 | INFO | No Futile columns found. 2025-11-04 01:22:29,238 | INFO | Columns with outlier percentage :- ColumnName OutlierPercentage 0 bedrooms 2.235772 1 garagepl 2.235772 2 bathrms 0.203252 3 lotsize 2.235772 4 stories 7.113821 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 01:22:29,466 | INFO | Feature Engineering started ... 2025-11-04 01:22:29,466 | INFO | Handling duplicate records present in dataset ... 2025-11-04 01:22:29,583 | INFO | Analysis completed. No action taken. 2025-11-04 01:22:29,583 | INFO | Total time to handle duplicate records: 0.12 sec 2025-11-04 01:22:29,584 | INFO | Starting customized anti-select columns ... 2025-11-04 01:22:29,950 | INFO | Updated dataset sample after performing anti-select columns: price lotsize bedrooms bathrms stories driveway recroom fullbase gashw airco garagepl prefarea homestyle 0 120000.0 5500.0 4 2 2 yes no yes no yes 1 yes bungalow 1 99000.0 8880.0 3 2 2 yes no yes no yes 1 no Eclectic 2 58000.0 4340.0 3 1 1 yes no no no no 0 no Eclectic 3 50000.0 3640.0 2 1 1 yes no no no no 1 no Classic 4 60000.0 5800.0 3 1 1 yes no no yes no 2 no Eclectic 5 54500.0 3150.0 2 2 1 no no yes no no 0 no Eclectic 6 70100.0 4200.0 3 1 2 yes no no no no 1 no Eclectic 7 44100.0 8100.0 2 1 1 yes no no no no 1 no Classic 8 27000.0 3649.0 2 1 1 yes no no no no 0 no Classic 9 48000.0 4120.0 2 1 2 yes no no no no 0 no Classic 492 rows X 13 columns 2025-11-04 01:22:30,305 | INFO | Handling less significant features from data ... 2025-11-04 01:22:34,541 | INFO | Analysis indicates all categorical columns are significant. No action Needed. 2025-11-04 01:22:34,541 | INFO | Total time to handle less significant features: 4.24 sec 2025-11-04 01:22:34,541 | INFO | Handling Date Features ... 2025-11-04 01:22:34,541 | INFO | Analysis Completed. Dataset does not contain any feature related to dates. No action needed. 2025-11-04 01:22:34,541 | INFO | Total time to handle date features: 0.00 sec 2025-11-04 01:22:34,541 | INFO | Proceeding with default option for missing value imputation. 2025-11-04 01:22:34,541 | INFO | Proceeding with default option for handling remaining missing values. 2025-11-04 01:22:34,541 | INFO | Checking Missing values in dataset ... 2025-11-04 01:22:36,626 | INFO | Analysis Completed. No Missing Values Detected. 2025-11-04 01:22:36,626 | INFO | Total time to find missing values in data: 2.09 sec 2025-11-04 01:22:36,627 | INFO | Imputing Missing Values ... 2025-11-04 01:22:36,627 | INFO | Analysis completed. No imputation required. 2025-11-04 01:22:36,627 | INFO | Time taken to perform imputation: 0.00 sec 2025-11-04 01:22:36,627 | INFO | No information provided for Equal-Width Transformation. 2025-11-04 01:22:36,628 | INFO | Variable-Width binning information:- ColumnName MinValue MaxValue Label 0 bedrooms 0 2 small_house 1 bedrooms 3 6 big_house 2 rows X 4 columns 2025-11-04 01:22:39,422 | INFO | Updated dataset sample after performing Variable-Width binning: driveway airco garagepl bathrms homestyle lotsize prefarea price automl_id gashw fullbase recroom bedrooms stories 3 yes yes 2 1 Eclectic 3000.0 no 58000.0 290 no yes no big_house 3 yes yes 2 2 bungalow 6360.0 yes 100500.0 179 no no no big_house 3 yes yes 0 2 Eclectic 6600.0 yes 89900.0 470 no no no big_house 3 yes yes 2 2 bungalow 7420.0 yes 190000.0 376 no no no big_house 3 yes no 1 1 Eclectic 5500.0 yes 89000.0 119 no no no big_house 3 yes no 0 1 Eclectic 4079.0 no 60000.0 455 no no no big_house 3 yes yes 0 1 Eclectic 5000.0 no 82000.0 199 no no no big_house 3 yes yes 0 1 Eclectic 2275.0 yes 52000.0 273 yes no no big_house 3 yes no 0 1 Classic 5200.0 no 40750.0 272 no no no big_house 3 yes no 0 1 Classic 2145.0 yes 49500.0 55 no no no big_house 492 rows X 14 columns 2025-11-04 01:22:39,537 | INFO | Skipping customized string manipulation. 2025-11-04 01:22:39,537 | INFO | Starting Customized Categorical Feature Encoding ... 2025-11-04 01:22:41,649 | INFO | Updated dataset sample after performing ordinal encoding: stories driveway garagepl bathrms lotsize prefarea price automl_id gashw bedrooms fullbase recroom homestyle airco yes 2 yes 1 1 4785.0 no 48500.0 475 no big_house yes yes 1 yes 3 yes 0 2 6350.0 no 88500.0 53 no big_house no yes 2 yes 1 yes 0 1 5800.0 no 70000.0 439 no small_house yes yes 2 yes 3 yes 0 1 5200.0 no 83000.0 69 no big_house no no 2 yes 2 yes 2 1 3500.0 no 48000.0 93 no big_house no no 1 yes 4 yes 0 1 4500.0 no 88000.0 22 no big_house no no 2 yes 4 yes 2 2 7475.0 no 120000.0 56 no big_house no no 0 yes 3 yes 2 2 7420.0 yes 190000.0 376 no big_house no no 0 yes 3 yes 2 2 4100.0 no 90000.0 294 no big_house no no 2 yes 4 yes 3 1 6600.0 yes 107000.0 90 no big_house no no 0 492 rows X 14 columns 2025-11-04 01:22:44,741 | INFO | Updated dataset sample after performing target encoding: airco driveway stories garagepl bathrms homestyle lotsize price automl_id gashw bedrooms fullbase recroom prefarea 62906.335979 yes yes 4 2 2 0 7475.0 120000.0 56 no big_house no no 62906.335979 yes yes 4 1 2 0 6000.0 107500.0 268 no big_house no no 62906.335979 yes yes 2 0 1 2 5040.0 60000.0 418 no big_house yes no 62906.335979 yes yes 2 0 1 2 3000.0 52000.0 36 no small_house no no 62906.335979 yes no 2 0 1 2 4095.0 70000.0 143 no big_house yes yes 62906.335979 yes yes 1 0 1 1 4960.0 44000.0 145 no small_house yes no 83851.724138 yes yes 2 0 1 0 6550.0 112500.0 322 no big_house yes no 83851.724138 yes yes 1 0 1 2 3600.0 58550.0 213 no big_house yes no 83851.724138 yes yes 2 0 1 2 5136.0 80000.0 309 no big_house yes yes 83851.724138 yes yes 2 1 2 0 6600.0 130000.0 328 no big_house yes yes 492 rows X 14 columns 2025-11-04 01:22:44,916 | INFO | Performing encoding for categorical columns ... 2025-11-04 01:22:47,436 | INFO | ONE HOT Encoding these Columns: ['airco', 'driveway', 'gashw', 'bedrooms', 'fullbase', 'recroom'] 2025-11-04 01:22:47,437 | INFO | Sample of dataset after performing one hot encoding: airco_0 airco_1 driveway_0 driveway_1 stories garagepl bathrms homestyle lotsize price automl_id gashw_0 gashw_1 bedrooms_0 bedrooms_1 fullbase_0 fullbase_1 recroom_0 recroom_1 prefarea 62906.335979 0 1 0 1 2 0 1 2 5040.0 60000.0 418 1 0 1 0 0 1 1 0 62906.335979 0 1 1 0 2 0 1 2 4095.0 70000.0 143 1 0 1 0 0 1 0 1 62906.335979 0 1 0 1 1 0 1 1 4960.0 44000.0 145 1 0 0 1 0 1 1 0 62906.335979 0 1 0 1 2 0 1 2 7152.0 55000.0 243 1 0 1 0 1 0 1 0 62906.335979 0 1 0 1 2 2 2 0 4600.0 127000.0 416 1 0 1 0 1 0 0 1 62906.335979 0 1 0 1 2 1 2 2 8880.0 99000.0 8 1 0 1 0 0 1 1 0 83851.724138 0 1 0 1 2 0 1 2 5136.0 80000.0 309 1 0 1 0 0 1 0 1 83851.724138 0 1 0 1 3 0 2 2 6500.0 95000.0 435 1 0 1 0 1 0 1 0 83851.724138 0 1 0 1 2 3 2 0 7500.0 174500.0 116 1 0 1 0 0 1 1 0 83851.724138 0 1 0 1 1 0 1 2 6420.0 85000.0 427 1 0 1 0 0 1 1 0 492 rows X 20 columns 2025-11-04 01:22:47,530 | INFO | Time taken to encode the columns: 2.61 sec 2025-11-04 01:22:47,530 | INFO | Starting customized mathematical transformation ... 2025-11-04 01:22:47,530 | INFO | Skipping customized mathematical transformation. 2025-11-04 01:22:47,530 | INFO | Starting customized non-linear transformation ... 2025-11-04 01:22:47,531 | INFO | Skipping customized non-linear transformation. 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 01:22:47,531 | INFO | Data preparation started ... 2025-11-04 01:22:47,531 | INFO | No information provided for performing customized feature scaling. Proceeding with default option. 2025-11-04 01:22:47,532 | INFO | Starting customized outlier processing ... 2025-11-04 01:22:50,825 | INFO | Columns with outlier percentage :- ColumnName OutlierPercentage 0 lotsize 9.552846 1 automl_id 9.756098 2 bathrms 2.235772 3 garagepl 2.235772 2025-11-04 01:22:53,373 | INFO | Sample of dataset after performing custom outlier filtering airco_0 airco_1 driveway_0 driveway_1 stories garagepl bathrms homestyle lotsize price automl_id gashw_0 gashw_1 bedrooms_0 bedrooms_1 fullbase_0 fullbase_1 recroom_0 recroom_1 prefarea 62906.335979 0 1 0 1 1 0 1 1 4960.0 44000.0 145 1 0 0 1 0 1 1 0 62906.335979 0 1 0 1 2 2 2 0 4600.0 127000.0 416 1 0 1 0 1 0 0 1 62906.335979 0 1 0 1 2 1 2 2 8880.0 99000.0 8 1 0 1 0 0 1 1 0 62906.335979 0 1 0 1 2 1 2 0 4560.0 123500.0 171 1 0 1 0 0 1 0 1 62906.335979 0 1 0 1 1 1 1 2 6000.0 98000.0 31 1 0 1 0 1 0 1 0 62906.335979 0 1 0 1 1 1 1 1 2684.0 46000.0 183 1 0 0 1 1 0 1 0 83851.724138 0 1 0 1 2 3 2 0 7500.0 174500.0 116 1 0 1 0 0 1 1 0 83851.724138 0 1 0 1 4 1 2 0 9000.0 103500.0 88 1 0 1 0 1 0 0 1 83851.724138 0 1 0 1 1 0 1 2 4815.0 69000.0 450 1 0 0 1 1 0 1 0 83851.724138 0 1 0 1 1 1 2 2 10500.0 94500.0 189 1 0 1 0 0 1 1 0 481 rows X 20 columns 2025-11-04 01:22:54,307 | INFO | Feature selection using rfe ... 2025-11-04 01:23:29,352 | INFO | feature selected by RFE: ['stories', 'bathrms', 'bedrooms_0', 'airco_0', 'garagepl', 'homestyle', 'fullbase_0', 'prefarea', 'lotsize'] 2025-11-04 01:23:29,354 | INFO | Total time taken by feature selection: 35.01 sec 2025-11-04 01:23:29,662 | INFO | Scaling Features of rfe data ... 2025-11-04 01:23:31,115 | INFO | columns that will be scaled: ['r_stories', 'r_bathrms', 'r_garagepl', 'r_homestyle', 'r_prefarea', 'r_lotsize'] 2025-11-04 01:23:33,186 | INFO | Dataset sample after scaling: r_bedrooms_0 automl_id price r_fullbase_0 r_airco_0 r_stories r_bathrms r_garagepl r_homestyle r_prefarea r_lotsize 0 0 6 54500.0 0 1 -0.924349 1.724879 -0.796258 0.734297 -0.554135 -0.940560 1 1 8 99000.0 0 0 0.242611 1.724879 0.389501 0.734297 -0.554135 1.763806 2 0 9 27000.0 1 1 -0.924349 -0.579751 -0.796258 -0.743515 -0.554135 -0.705049 3 1 10 70100.0 1 1 0.242611 -0.579751 0.389501 0.734297 -0.554135 -0.444995 4 1 12 58000.0 1 1 -0.924349 -0.579751 -0.796258 0.734297 -0.554135 -0.378920 5 1 13 60000.0 1 1 -0.924349 -0.579751 1.575259 0.734297 -0.554135 0.310150 6 1 11 83900.0 1 1 1.409572 -0.579751 1.575259 0.734297 1.804616 2.981478 7 1 7 80000.0 1 1 0.242611 1.724879 0.389501 0.734297 -0.554135 2.528391 8 0 5 50000.0 1 1 -0.924349 -0.579751 0.389501 -0.743515 -0.554135 -0.709296 9 0 4 48000.0 1 1 0.242611 -0.579751 -0.796258 -0.743515 -0.554135 -0.482753 481 rows X 11 columns 2025-11-04 01:23:33,772 | INFO | Total time taken by feature scaling: 4.11 sec 2025-11-04 01:23:33,773 | INFO | Scaling Features of pca data ... 2025-11-04 01:23:35,626 | INFO | columns that will be scaled: ['prefarea', 'stories', 'garagepl', 'bathrms', 'homestyle', 'lotsize'] 2025-11-04 01:23:37,813 | INFO | Dataset sample after scaling: fullbase_1 bedrooms_1 driveway_0 airco_0 recroom_1 airco_1 gashw_0 price automl_id gashw_1 fullbase_0 driveway_1 recroom_0 bedrooms_0 prefarea stories garagepl bathrms homestyle lotsize 0 1 1 0 0 0 1 1 44000.0 145 0 0 1 1 0 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 -0.086301 1 0 0 0 0 1 1 1 127000.0 416 0 1 1 0 1 -0.554135 0.242611 1.575259 1.724879 -2.221327 -0.256209 2 1 0 0 0 0 1 1 99000.0 8 0 0 1 1 1 -0.554135 0.242611 0.389501 1.724879 0.734297 1.763806 3 1 0 0 0 1 1 1 123500.0 171 0 0 1 0 1 -0.554135 0.242611 0.389501 1.724879 -2.221327 -0.275087 4 0 0 0 0 0 1 1 98000.0 31 0 1 1 1 1 -0.554135 -0.924349 0.389501 -0.579751 0.734297 0.404544 5 0 1 0 0 0 1 1 46000.0 183 0 1 1 1 0 -0.554135 -0.924349 0.389501 -0.579751 -0.743515 -1.160496 6 1 0 0 0 0 1 1 174500.0 116 0 0 1 1 1 1.804616 0.242611 2.761017 1.724879 -2.221327 1.112493 7 0 0 0 0 1 1 1 103500.0 88 0 1 1 0 1 1.804616 2.576532 0.389501 1.724879 -2.221327 1.820442 8 0 1 0 0 0 1 1 69000.0 450 0 1 1 1 0 1.804616 -0.924349 -0.796258 -0.579751 0.734297 -0.154736 9 1 0 0 0 0 1 1 94500.0 189 0 0 1 1 1 1.804616 -0.924349 0.389501 1.724879 0.734297 2.528391 481 rows X 20 columns 2025-11-04 01:23:38,447 | INFO | Total time taken by feature scaling: 4.67 sec 2025-11-04 01:23:38,448 | INFO | Dimension Reduction using pca ... 2025-11-04 01:23:39,107 | INFO | PCA columns: ['col_0', 'col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6', 'col_7', 'col_8', 'col_9'] 2025-11-04 01:23:39,108 | INFO | Total time taken by PCA: 0.66 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation 2025-11-04 01:23:39,514 | INFO | Model Training started ... 2025-11-04 01:23:39,558 | INFO | Starting customized hyperparameter update ... 2025-11-04 01:23:39,558 | INFO | Skipping customized hyperparameter tuning 2025-11-04 01:23:39,559 | INFO | Hyperparameters used for model training: 2025-11-04 01:23:39,559 | INFO | Model: decision_forest 2025-11-04 01:23:39,559 | INFO | Hyperparameters: {'response_column': 'price', 'name': 'decision_forest', 'tree_type': 'Regression', 'min_impurity': (0.0, 0.1, 0.2, 0.3), 'max_depth': (5, 3, 4, 7, 8), 'min_node_size': (1, 2, 3, 4), 'num_trees': (-1,), 'seed': 42} 2025-11-04 01:23:39,559 | INFO | Total number of models for decision_forest: 80 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 2025-11-04 01:23:39,560 | INFO | Model: xgboost 2025-11-04 01:23:39,560 | INFO | Hyperparameters: {'response_column': 'price', 'name': 'xgboost', 'model_type': 'Regression', 'column_sampling': (1, 0.6), 'min_impurity': (0.0, 0.1, 0.2, 0.3), 'lambda1': (1.0, 1.0, 10.0, 100.0), 'shrinkage_factor': (0.5, 0.01, 0.05, 0.1), 'max_depth': (5, 3, 4, 7, 8), 'min_node_size': (1, 2, 3, 4), 'iter_num': (10, 20, 30, 40), 'num_boosted_trees': (-1, 20, 50, 100), 'seed': 42} 2025-11-04 01:23:39,720 | INFO | Total number of models for xgboost: 40960 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 2025-11-04 01:23:39,721 | INFO | Performing hyperparameter tuning ... 2025-11-04 01:23:40,813 | INFO | Model training for decision_forest 2025-11-04 01:23:57,962 | INFO | ---------------------------------------------------------------------------------------------------- 2025-11-04 01:23:57,962 | INFO | Model training for xgboost 2025-11-04 01:24:17,095 | INFO | ---------------------------------------------------------------------------------------------------- 2025-11-04 01:24:17,097 | INFO | Leaderboard RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE ... ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_0 rfe 7711.646791 1.006034e+08 0.024802 ... 32571.392060 0.868742 0.871048 1386.666530 0.023267 0.866233 1 2 DECISIONFOREST_2 rfe 8570.760495 1.165783e+08 0.025961 ... 34000.000000 0.847899 0.848303 1556.243777 0.025133 0.844992 2 3 XGBOOST_1 pca 8397.288359 1.461547e+08 0.033499 ... 53787.874836 0.809310 0.812617 1957.464971 0.030490 0.805253 3 4 DECISIONFOREST_0 rfe 9012.190907 1.518862e+08 0.027755 ... 55000.000000 0.801832 0.802970 1807.795304 0.026938 0.798046 4 5 XGBOOST_2 rfe 10662.822187 2.708815e+08 0.040123 ... 82415.914398 0.646578 0.652841 3060.543638 0.040770 0.639824 5 6 DECISIONFOREST_3 pca 10714.325844 2.792791e+08 0.048732 ... 72750.000000 0.635621 0.636265 3329.871923 0.045563 0.627868 6 7 XGBOOST_3 pca 11629.208492 3.193868e+08 0.047100 ... 81429.175364 0.583292 0.600407 3646.398180 0.048086 0.574426 7 8 DECISIONFOREST_1 pca 12298.340849 3.915081e+08 0.064405 ... 72750.000000 0.489194 0.494876 4503.740377 0.058413 0.478326 8 9 XGBOOST_4 rfe 14526.292231 4.623473e+08 0.077264 ... 94901.156086 0.396770 0.401454 5659.836390 0.078339 0.385243 [9 rows x 16 columns] 9 rows X 16 columns 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation >>> Completed: |⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿| 100% - 18/18 - Display model leaderboard.
>>> aml.leaderboard()
RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE ... ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_0 rfe 7711.646791 1.006034e+08 0.024802 ... 32571.392060 0.868742 0.871048 1386.666530 0.023267 0.866233 1 2 DECISIONFOREST_2 rfe 8570.760495 1.165783e+08 0.025961 ... 34000.000000 0.847899 0.848303 1556.243777 0.025133 0.844992 2 3 XGBOOST_1 pca 8397.288359 1.461547e+08 0.033499 ... 53787.874836 0.809310 0.812617 1957.464971 0.030490 0.805253 3 4 DECISIONFOREST_0 rfe 9012.190907 1.518862e+08 0.027755 ... 55000.000000 0.801832 0.802970 1807.795304 0.026938 0.798046 4 5 XGBOOST_2 rfe 10662.822187 2.708815e+08 0.040123 ... 82415.914398 0.646578 0.652841 3060.543638 0.040770 0.639824 5 6 DECISIONFOREST_3 pca 10714.325844 2.792791e+08 0.048732 ... 72750.000000 0.635621 0.636265 3329.871923 0.045563 0.627868 6 7 XGBOOST_3 pca 11629.208492 3.193868e+08 0.047100 ... 81429.175364 0.583292 0.600407 3646.398180 0.048086 0.574426 7 8 DECISIONFOREST_1 pca 12298.340849 3.915081e+08 0.064405 ... 72750.000000 0.489194 0.494876 4503.740377 0.058413 0.478326 8 9 XGBOOST_4 rfe 14526.292231 4.623473e+08 0.077264 ... 94901.156086 0.396770 0.401454 5659.836390 0.078339 0.385243
- Display the best performing model.
>>> aml.leader()
RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE ... ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_0 rfe 7711.646791 1.006034e+08 0.024802 ... 32571.39206 0.868742 0.871048 1386.66653 0.023267 0.866233
- Display hyperparameters for trained model.
- Display model hyperparameters for rank 2.
>>> aml.model_hyperparameters(rank=2)
{'response_column': 'price', 'name': 'decision_forest', 'tree_type': 'Regression', 'min_impurity': 0.0, 'max_depth': 5, 'min_node_size': 2, 'num_trees': -1, 'seed': 42, 'persist': False} - Display model hyperparameters for rank 3.
>>> aml.model_hyperparameters(rank=3)
{'response_column': 'price', 'name': 'xgboost', 'model_type': 'Regression', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 1.0, 'shrinkage_factor': 0.5, 'max_depth': 5, 'min_node_size': 1, 'iter_num': 10, 'num_boosted_trees': -1, 'seed': 42, 'persist': False}
- Display model hyperparameters for rank 2.
- Generate prediction on test dataset using best performing model.
>>> prediction = aml.predict(housing_test)
2025-11-04 01:28:14,601 | INFO | Data Transformation started ... 2025-11-04 01:28:14,601 | INFO | Performing transformation carried out in feature engineering phase ... 2025-11-04 01:28:16,160 | INFO | Updated dataset after performing customized variable width bin-code transformation : driveway airco garagepl bathrms homestyle lotsize prefarea price automl_id gashw fullbase recroom sn bedrooms stories 3 yes no 0 1 Eclectic 6420.0 yes 87500.0 53 no yes no 408 big_house 1 yes yes 1 1 Eclectic 5885.0 no 64000.0 28 no no no 306 small_house 1 yes yes 0 1 Eclectic 6825.0 yes 77500.0 32 no yes yes 403 big_house 1 yes no 0 2 Eclectic 4100.0 no 64900.0 24 no yes yes 274 small_house 1 yes no 2 1 Classic 3450.0 no 48500.0 17 no yes no 251 big_house 1 yes no 2 1 Eclectic 3520.0 yes 51900.0 45 no no no 441 big_house 1 yes no 0 1 Classic 6000.0 no 41000.0 14 no no no 260 small_house 1 yes no 2 1 Eclectic 7980.0 no 78500.0 49 no no no 353 big_house 1 yes no 0 1 Eclectic 2787.0 yes 60500.0 19 no yes no 472 big_house 1 yes no 1 1 Eclectic 9000.0 yes 90000.0 25 no yes no 411 big_house 46 rows X 15 columns 2025-11-04 01:28:17,829 | INFO | Updated dataset after performing customized categorical encoding : airco driveway stories garagepl bathrms homestyle lotsize price automl_id gashw bedrooms fullbase recroom sn prefarea 62906.335979 yes yes 2 1 1 2 3162.0 63900.0 33 no big_house no no 161 62906.335979 no yes 1 0 1 2 4080.0 55000.0 5 no small_house no no 301 62906.335979 no yes 1 2 1 1 3450.0 48500.0 17 no big_house yes no 251 62906.335979 no yes 1 2 1 1 3000.0 26000.0 39 no small_house yes no 239 62906.335979 no no 1 0 1 1 5076.0 43000.0 40 no big_house no no 111 62906.335979 no no 1 0 1 1 3970.0 32500.0 20 no small_house no no 234 83851.724138 yes yes 1 0 1 2 6825.0 77500.0 32 no big_house yes yes 403 83851.724138 no yes 2 0 1 2 2176.0 55000.0 4 no small_house no yes 469 83851.724138 no yes 2 0 1 1 2610.0 49000.0 6 no big_house yes no 463 83851.724138 no yes 1 0 1 1 2398.0 44555.0 13 no big_house no no 459 46 rows X 15 columns 2025-11-04 01:28:19,042 | INFO | Updated dataset after performing categorical encoding : airco_0 airco_1 driveway_0 driveway_1 stories garagepl bathrms homestyle lotsize price automl_id gashw_0 gashw_1 bedrooms_0 bedrooms_1 fullbase_0 fullbase_1 recroom_0 recroom_1 sn prefarea 62906.335979 1 0 0 1 1 2 1 1 3450.0 48500.0 17 1 0 1 0 0 1 1 0 251 62906.335979 1 0 1 0 1 0 1 1 5076.0 43000.0 40 1 0 1 0 1 0 1 0 111 62906.335979 1 0 1 0 1 0 1 1 3970.0 32500.0 20 1 0 0 1 1 0 1 0 234 62906.335979 1 0 0 1 4 0 1 2 5000.0 80000.0 51 1 0 1 0 1 0 1 0 317 62906.335979 1 0 0 1 2 1 1 1 2650.0 40000.0 29 1 0 1 0 0 1 1 0 142 62906.335979 1 0 0 1 2 0 1 2 4360.0 61000.0 10 1 0 1 0 1 0 1 0 255 83851.724138 1 0 0 1 2 0 1 1 2610.0 49000.0 6 1 0 1 0 0 1 1 0 463 83851.724138 1 0 0 1 1 2 1 2 3520.0 51900.0 45 1 0 1 0 1 0 1 0 441 83851.724138 1 0 0 1 1 1 1 2 9000.0 90000.0 25 1 0 1 0 0 1 1 0 411 83851.724138 1 0 0 1 3 0 1 2 6420.0 87500.0 53 1 0 1 0 0 1 1 0 408 46 rows X 21 columns 2025-11-04 01:28:19,809 | INFO | Updated dataset after performing customized anti-selection : prefarea airco_0 airco_1 driveway_0 driveway_1 stories garagepl bathrms homestyle lotsize price automl_id gashw_0 gashw_1 bedrooms_0 bedrooms_1 fullbase_0 fullbase_1 recroom_0 recroom_1 0 83851.724138 1 0 0 1 1 1 1 2 9000.0 90000.0 25 1 0 1 0 0 1 1 0 1 83851.724138 1 0 0 1 1 0 1 2 2787.0 60500.0 19 1 0 1 0 0 1 1 0 2 83851.724138 0 1 0 1 2 2 1 2 6862.0 69000.0 15 1 0 1 0 1 0 1 0 3 83851.724138 0 1 0 1 1 2 1 2 7410.0 92500.0 36 1 0 1 0 0 1 0 1 4 83851.724138 1 0 0 1 1 0 1 1 2398.0 44555.0 13 1 0 1 0 1 0 1 0 5 83851.724138 1 0 0 1 2 0 1 2 2176.0 55000.0 4 1 0 0 1 1 0 0 1 6 62906.335979 0 1 0 1 2 1 1 2 3162.0 63900.0 33 1 0 1 0 1 0 1 0 7 62906.335979 1 0 0 1 1 0 1 2 4080.0 55000.0 5 1 0 0 1 1 0 1 0 8 62906.335979 1 0 0 1 1 2 1 1 3450.0 48500.0 17 1 0 1 0 0 1 1 0 9 62906.335979 1 0 0 1 1 2 1 1 3000.0 26000.0 39 1 0 0 1 0 1 1 0 46 rows X 20 columns 2025-11-04 01:28:20,104 | INFO | Performing transformation carried out in data preparation phase ... 2025-11-04 01:28:20,933 | INFO | Updated dataset after performing RFE feature selection: automl_id stories bathrms bedrooms_0 airco_0 garagepl homestyle fullbase_0 prefarea lotsize price 0 29 2 1 1 1 1 1 0 62906.336 2650.0 40000.0 1 23 2 2 1 1 1 2 0 62906.336 2817.0 78500.0 2 34 2 2 1 1 0 2 1 62906.336 4300.0 86900.0 3 27 2 1 1 1 0 1 1 62906.336 3750.0 43000.0 4 7 2 1 1 1 0 2 1 62906.336 5400.0 70000.0 5 12 2 1 1 1 0 2 0 62906.336 10700.0 72000.0 6 20 1 1 0 1 0 1 1 62906.336 3970.0 32500.0 7 24 1 2 0 1 0 2 0 62906.336 4100.0 64900.0 8 59 1 1 0 1 0 1 1 62906.336 4040.0 47000.0 9 21 1 1 0 1 0 1 1 62906.336 3500.0 44500.0 46 rows X 11 columns 2025-11-04 01:28:21,773 | INFO | Updated dataset after performing scaling on RFE selected features : r_bedrooms_0 automl_id price r_fullbase_0 r_airco_0 r_stories r_bathrms r_garagepl r_homestyle r_prefarea r_lotsize 0 1 29 40000.0 0 1 0.242611 -0.579751 0.389501 -0.743515 -0.554135 -1.176543 1 1 23 78500.0 0 1 0.242611 1.724879 0.389501 0.734297 -0.554135 -1.097724 2 1 34 86900.0 1 1 0.242611 1.724879 -0.796258 0.734297 -0.554135 -0.397799 3 1 27 43000.0 1 1 0.242611 -0.579751 -0.796258 -0.743515 -0.554135 -0.657380 4 1 7 70000.0 1 1 0.242611 -0.579751 -0.796258 0.734297 -0.554135 0.121364 5 1 12 72000.0 0 1 0.242611 -0.579751 -0.796258 0.734297 -0.554135 2.622784 6 0 20 32500.0 1 1 -0.924349 -0.579751 -0.796258 -0.743515 -0.554135 -0.553547 7 0 24 64900.0 0 1 -0.924349 1.724879 -0.796258 0.734297 -0.554135 -0.492192 8 0 59 47000.0 1 1 -0.924349 -0.579751 -0.796258 -0.743515 -0.554135 -0.520510 9 0 21 44500.0 1 1 -0.924349 -0.579751 -0.796258 -0.743515 -0.554135 -0.775371 46 rows X 11 columns 2025-11-04 01:28:23,133 | INFO | Updated dataset after performing scaling for PCA feature selection : fullbase_1 bedrooms_1 driveway_0 airco_0 recroom_1 airco_1 gashw_0 price automl_id gashw_1 fullbase_0 driveway_1 recroom_0 bedrooms_0 prefarea stories garagepl bathrms homestyle lotsize 0 0 1 0 1 0 0 1 47000.0 59 0 1 1 1 0 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 -0.520510 1 0 1 0 0 0 1 1 37900.0 37 0 1 1 1 0 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 -0.924041 2 1 1 0 0 0 1 1 68000.0 11 0 0 1 1 0 -0.554135 -0.924349 1.575259 -0.579751 0.734297 1.898788 3 0 1 0 1 0 0 1 42000.0 44 0 1 1 1 0 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 -0.086301 4 0 1 0 1 1 0 1 55000.0 4 0 1 1 0 0 1.804616 0.242611 -0.796258 -0.579751 0.734297 -1.400254 5 0 1 0 1 0 0 1 41000.0 14 0 1 1 1 0 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 0.404544 6 0 0 1 1 0 0 1 43000.0 40 0 1 0 1 1 -0.554135 -0.924349 -0.796258 -0.579751 -0.743515 -0.031553 7 0 0 0 1 0 0 1 62500.0 18 0 1 1 1 1 -0.554135 0.242611 -0.796258 -0.579751 0.734297 -0.586585 8 1 0 0 1 0 0 1 40000.0 29 0 0 1 1 1 -0.554135 0.242611 0.389501 -0.579751 -0.743515 -1.176543 9 0 0 0 1 0 0 1 61000.0 10 0 1 1 1 1 -0.554135 0.242611 -0.796258 -0.579751 0.734297 -0.369481 46 rows X 20 columns 2025-11-04 01:28:23,602 | INFO | Updated dataset after performing PCA feature selection : automl_id col_0 col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8 col_9 price 0 20 -1.781932 0.357389 -0.804165 0.781302 0.423531 0.385514 -0.392667 -0.173088 0.116668 -0.422125 32500.0 1 40 -1.237917 0.311520 -0.691958 0.630772 0.148798 0.582022 -0.088701 0.240978 1.212262 -0.546010 43000.0 2 24 -0.537790 -0.121912 0.965847 -0.607395 2.236753 0.806179 0.160764 0.209506 -1.265151 -0.373994 64900.0 3 18 -1.134034 0.386400 0.555704 -0.504103 -0.711792 0.072424 -0.284142 0.277344 0.185648 0.015771 62500.0 4 59 -1.595203 0.216056 -0.793669 0.752214 0.263594 0.451237 -0.432608 -0.143301 -0.539837 0.138279 47000.0 5 29 -0.620205 0.622530 -0.446559 0.092160 0.180652 -1.147594 0.789457 0.734782 0.044372 0.733643 40000.0 6 21 -1.717824 0.310649 -0.738037 0.754847 0.325761 0.275516 -0.430316 -0.180066 -0.561117 0.133578 44500.0 7 10 -1.029580 0.305820 0.508314 -0.506346 -0.764749 0.222112 -0.286095 0.308663 0.203776 0.019775 61000.0 8 37 -1.491236 0.481402 -0.667688 0.699467 0.245828 0.252861 0.148402 -1.429806 -0.468139 0.210214 37900.0 9 23 0.199440 0.655693 0.973420 -1.287750 1.822717 -0.705320 0.523943 0.663558 0.058868 -1.033697 78500.0 10 rows X 12 columns 2025-11-04 01:28:23,901 | INFO | Data Transformation completed.⫿⫿⫿⫿⫿| 100% - 14/14 2025-11-04 01:28:24,428 | INFO | Following model is being picked for evaluation: 2025-11-04 01:28:24,429 | INFO | Model ID : XGBOOST_0 2025-11-04 01:28:24,430 | INFO | Feature Selection Method : rfe 2025-11-04 01:28:24,960 | INFO | Applying SHAP for Model Interpretation... 2025-11-04 01:28:26,853 | INFO | SHAP Analysis Completed. Feature Importance Available. /root/automl_testing/pyTeradata/teradataml/automl/model_evaluation.py:380: UserWarning: FigureCanvasAgg is non-interactive, and thus cannot be shown plt.show() 2025-11-04 01:28:29,243 | INFO | Prediction : automl_id Prediction Confidence_Lower Confidence_upper price 0 59 44644.431089 44644.431089 44644.431089 47000.0 1 37 46045.859757 46045.859757 46045.859757 37900.0 2 11 75321.783121 75321.783121 75321.783121 68000.0 3 44 42945.364286 42945.364286 42945.364286 42000.0 4 4 55707.416681 55707.416681 55707.416681 55000.0 5 14 42619.512243 42619.512243 42619.512243 41000.0 6 40 39995.145103 39995.145103 39995.145103 43000.0 7 18 62810.532437 62810.532437 62810.532437 62500.0 8 29 40034.885289 40034.885289 40034.885289 40000.0 9 10 62810.532437 62810.532437 62810.532437 61000.0>>> prediction.head()
automl_id Prediction Confidence_Lower Confidence_upper price 0 29 40034.885289 40034.885289 40034.885289 40000.0 1 23 65274.280393 65274.280393 65274.280393 78500.0 2 34 69785.116206 69785.116206 69785.116206 86900.0 3 27 43980.181898 43980.181898 43980.181898 43000.0 4 7 61245.505123 61245.505123 61245.505123 70000.0 5 12 76062.036134 76062.036134 76062.036134 72000.0 6 20 42655.233172 42655.233172 42655.233172 32500.0 7 24 61885.319803 61885.319803 61885.319803 64900.0 8 59 44644.431089 44644.431089 44644.431089 47000.0 9 21 41295.905196 41295.905196 41295.905196 44500.0
- Generate evaluation metrics on test dataset using best performing model.
>>> performance_metrics = aml.evaluate(housing_test)
2025-11-04 01:30:44,506 | INFO | Skipping data transformation as data is already transformed. 2025-11-04 01:30:45,050 | INFO | Following model is being picked for evaluation: 2025-11-04 01:30:45,051 | INFO | Model ID : XGBOOST_0 2025-11-04 01:30:45,051 | INFO | Feature Selection Method : rfe 2025-11-04 01:30:46,408 | INFO | Performance Metrics : MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD 0 6215.716247 7.214750e+07 0.021857 11.161124 -3.35438 8493.968396 0.14784 29444.576968 0.77593 0.776068 1173.996768 0.021215>>> performance_metrics
MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD 0 6215.716247 7.214750e+07 0.021857 11.161124 -3.35438 8493.968396 0.14784 29444.576968 0.77593 0.776068 1173.996768 0.021215