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)
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 Feature Exploration started ... Data Overview: Total Rows in the data: 492 Total Columns in the data: 14 Column Summary: ColumnName Datatype NonNullCount NullCount BlankCount ZeroCount PositiveCount NegativeCount NullPercentage NonNullPercentage homestyle VARCHAR(20) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 airco VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 price FLOAT 492 0 None 0 492 0 0.0 100.0 lotsize FLOAT 492 0 None 0 492 0 0.0 100.0 fullbase VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 bathrms INTEGER 492 0 None 0 492 0 0.0 100.0 recroom VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 bedrooms INTEGER 492 0 None 0 492 0 0.0 100.0 driveway VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 prefarea VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 stories INTEGER 492 0 None 0 492 0 0.0 100.0 sn INTEGER 492 0 None 0 492 0 0.0 100.0 garagepl INTEGER 492 0 None 270 222 0 0.0 100.0 gashw VARCHAR(10) CHARACTER SET LATIN 492 0 0 None None None 0.0 100.0 sn price lotsize bedrooms bathrms stories garagepl func 50% 274.000 62000.000 4616.000 3.000 1.000 2.000 0.000 count 492.000 492.000 492.000 492.000 492.000 492.000 492.000 mean 272.943 68100.396 5181.795 2.965 1.293 1.803 0.685 min 1.000 25000.000 1650.000 1.000 1.000 1.000 0.000 max 546.000 190000.000 16200.000 6.000 4.000 4.000 3.000 75% 413.250 82000.000 6370.000 3.000 2.000 2.000 1.000 25% 132.500 49975.000 3600.000 2.000 1.000 1.000 0.000 std 159.501 26472.496 2182.443 0.731 0.510 0.861 0.854 Statistics of Data: func sn price lotsize bedrooms bathrms stories garagepl 50% 274 62000 4616 3 1 2 0 count 492 492 492 492 492 492 492 mean 272.943 68100.396 5181.795 2.965 1.293 1.803 0.685 min 1 25000 1650 1 1 1 0 max 546 190000 16200 6 4 4 3 75% 413.25 82000 6370 3 2 2 1 25% 132.5 49975 3600 2 1 1 0 std 159.501 26472.496 2182.443 0.731 0.51 0.861 0.854 Categorical Columns with their Distinct values: ColumnName DistinctValueCount driveway 2 recroom 2 fullbase 2 gashw 2 airco 2 prefarea 2 homestyle 3 No Futile columns found. Target Column Distribution: Columns with outlier percentage :- ColumnName OutlierPercentage 0 stories 7.113821 1 bathrms 0.203252 2 bedrooms 2.235772 3 garagepl 2.235772 4 lotsize 2.235772 5 price 2.439024 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Feature Engineering started ... Handling duplicate records present in dataset ... Analysis completed. No action taken. Total time to handle duplicate records: 1.71 sec Starting customized anti-select columns ... Updated dataset sample after performing anti-select columns: price lotsize bedrooms bathrms stories driveway recroom fullbase gashw airco garagepl prefarea homestyle 99000.0 8880.0 3 2 2 yes no yes no yes 1 no Eclectic 63900.0 6360.0 2 1 1 yes no yes no yes 1 no Eclectic 88000.0 4500.0 3 1 4 yes no no no yes 0 no Eclectic 50000.0 3640.0 2 1 1 yes no no no no 1 no Classic 48000.0 4120.0 2 1 2 yes no no no no 0 no Classic 27000.0 3649.0 2 1 1 yes no no no no 0 no Classic 58000.0 4340.0 3 1 1 yes no no no no 0 no Eclectic 87000.0 8372.0 3 1 3 yes no no no yes 2 no Eclectic 49500.0 5320.0 2 1 1 yes no no no no 1 yes Classic 70100.0 4200.0 3 1 2 yes no no no no 1 no Eclectic 492 rows X 13 columns Handling less significant features from data ... Analysis indicates all categorical columns are significant. No action Needed. Total time to handle less significant features: 18.82 sec Handling Date Features ... Analysis Completed. Dataset does not contain any feature related to dates. No action needed. Total time to handle date features: 0.00 sec Proceeding with default option for missing value imputation. Proceeding with default option for handling remaining missing values. Checking Missing values in dataset ... Analysis Completed. No Missing Values Detected. Total time to find missing values in data: 8.68 sec Imputing Missing Values ... Analysis completed. No imputation required. Time taken to perform imputation: 0.01 sec No information provided for Equal-Width Transformation. 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 Updated dataset sample after performing Variable-Width binning: bathrms stories gashw airco fullbase id homestyle lotsize garagepl prefarea driveway recroom price bedrooms 3 2 no no yes 194 bungalow 5960.0 1 no yes yes 117000.0 big_house 3 2 no no yes 291 Eclectic 3300.0 0 no yes no 79000.0 big_house 3 4 no yes no 76 bungalow 8580.0 2 yes yes no 145000.0 big_house 3 2 no no no 92 Classic 3630.0 0 no no yes 38000.0 big_house 3 2 yes no yes 66 bungalow 6000.0 2 no yes yes 138300.0 big_house 3 2 no no no 222 bungalow 16200.0 0 no yes no 145000.0 big_house 3 2 no no yes 118 Eclectic 4410.0 2 no yes no 71000.0 big_house 1 1 no yes no 195 Classic 2684.0 1 no yes no 46000.0 small_house 1 2 no no no 83 Classic 4370.0 0 no yes no 46000.0 big_house 1 2 no no yes 242 Classic 3970.0 0 no yes no 32500.0 big_house 492 rows X 14 columns Skipping customized string manipulation. Starting Customized Categorical Feature Encoding ... Updated dataset sample after performing ordinal encoding: bathrms stories gashw airco fullbase id lotsize bedrooms garagepl prefarea driveway recroom price homestyle 3 4 no yes no 76 8580.0 big_house 2 yes yes no 145000.0 0 3 2 no no no 73 2610.0 big_house 0 no no no 60000.0 2 3 2 no no no 222 16200.0 big_house 0 no yes no 145000.0 0 3 2 no no yes 118 4410.0 big_house 2 no yes no 71000.0 2 3 2 no no yes 291 3300.0 big_house 0 no yes no 79000.0 2 3 2 no no yes 194 5960.0 big_house 1 no yes yes 117000.0 0 3 2 no no no 510 8580.0 big_house 2 no yes no 92000.0 2 1 1 no no no 206 4320.0 big_house 0 yes yes no 67000.0 2 1 2 no no yes 169 5010.0 big_house 0 no yes no 66000.0 2 1 2 no yes no 53 4400.0 big_house 2 yes yes no 79500.0 2 492 rows X 14 columns Updated dataset sample after performing target encoding: prefarea bathrms stories gashw airco fullbase id homestyle lotsize bedrooms garagepl driveway recroom price 83851.72413793103 1 1 no yes no 96 2 10240.0 small_house 2 yes no 68000.0 83851.72413793103 1 1 no no yes 233 2 6900.0 big_house 0 yes yes 78900.0 83851.72413793103 1 1 no yes yes 185 2 6360.0 big_house 2 yes yes 82000.0 83851.72413793103 1 2 no yes yes 375 2 5136.0 big_house 0 yes yes 80000.0 83851.72413793103 1 2 no yes yes 378 2 8400.0 big_house 2 yes yes 75000.0 83851.72413793103 1 3 no yes no 285 2 6100.0 big_house 0 yes yes 78000.0 62906.33597883598 1 1 no no yes 211 2 3540.0 small_house 0 no yes 72000.0 62906.33597883598 1 1 no no no 247 1 3360.0 small_house 1 yes no 30000.0 62906.33597883598 1 1 no no no 395 1 2700.0 small_house 0 no no 42000.0 62906.33597883598 1 2 no yes yes 55 1 3180.0 big_house 0 yes no 47000.0 492 rows X 14 columns Performing encoding for categorical columns ... ONE HOT Encoding these Columns: ['gashw', 'airco', 'fullbase', 'bedrooms', 'driveway', 'recroom'] Sample of dataset after performing one hot encoding: prefarea bathrms stories gashw_0 gashw_1 airco_0 airco_1 fullbase_0 fullbase_1 id homestyle lotsize bedrooms_0 bedrooms_1 garagepl driveway_0 driveway_1 recroom_0 recroom_1 price 83851.72413793103 1 1 1 0 0 1 0 1 185 2 6360.0 1 0 2 0 1 0 1 82000.0 83851.72413793103 1 2 1 0 0 1 0 1 378 2 8400.0 1 0 2 0 1 0 1 75000.0 83851.72413793103 1 3 1 0 0 1 1 0 285 2 6100.0 1 0 0 0 1 0 1 78000.0 83851.72413793103 1 1 1 0 0 1 0 1 139 2 11175.0 1 0 1 0 1 1 0 100000.0 83851.72413793103 1 2 1 0 1 0 0 1 58 2 3400.0 1 0 2 0 1 1 0 61100.0 83851.72413793103 1 1 1 0 1 0 1 0 464 2 6040.0 1 0 2 0 1 1 0 69000.0 62906.33597883598 1 1 1 0 1 0 1 0 395 1 2700.0 0 1 0 1 0 1 0 42000.0 62906.33597883598 1 1 1 0 1 0 1 0 119 2 6720.0 1 0 0 0 1 1 0 70000.0 62906.33597883598 1 2 1 0 1 0 1 0 144 1 4840.0 0 1 0 0 1 1 0 35000.0 62906.33597883598 1 1 1 0 0 1 1 0 234 0 8880.0 0 1 1 0 1 1 0 101000.0 492 rows X 20 columns Time taken to encode the columns: 14.42 sec Starting customized mathematical transformation ... Skipping customized mathematical transformation. Starting customized non-linear transformation ... Skipping customized non-linear transformation. 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Data preparation started ... No information provided for performing customized feature scaling. Proceeding with default option. Starting customized outlier processing ... Columns with outlier percentage :- ColumnName OutlierPercentage 0 id 9.756098 1 lotsize 9.552846 2 bathrms 2.235772 3 garagepl 2.235772 4 price 8.739837 Feature selection using lasso ... feature selected by lasso: ['recroom_1', 'stories', 'gashw_0', 'bedrooms_0', 'garagepl', 'fullbase_1', 'bathrms', 'airco_0', 'recroom_0', 'airco_1', 'driveway_1', 'gashw_1', 'bedrooms_1', 'homestyle', 'fullbase_0', 'driveway_0', 'prefarea', 'lotsize'] Total time taken by feature selection: 0.96 sec scaling Features of lasso data ... columns that will be scaled: ['stories', 'garagepl', 'bathrms', 'homestyle', 'prefarea', 'lotsize'] Dataset sample after scaling: recroom_1 airco_0 recroom_0 gashw_0 airco_1 id driveway_1 gashw_1 bedrooms_1 fullbase_0 bedrooms_0 fullbase_1 price driveway_0 stories garagepl bathrms homestyle prefarea lotsize 0 1 1 1 0 10 0 0 1 0 0 1 54500.0 1 -0.9243489557048288 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.9405595540664473 0 0 1 1 1 12 1 0 1 0 0 1 63900.0 0 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 0.5744513081034206 0 0 1 1 1 13 1 0 0 0 1 1 99000.0 0 0.24261127446320968 0.38950061132561975 1.7248787237282124 0.7342974433930187 -0.5541346563708683 1.7638056298068683 0 1 1 1 0 14 1 0 1 1 0 0 48000.0 0 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.48275253341075514 0 0 1 1 1 16 1 0 0 1 1 0 87000.0 0 1.4095715046312483 1.5752588015004494 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 1.5240469014634748 0 0 1 1 1 17 0 0 0 0 1 1 57000.0 1 0.24261127446320968 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.303405453153886 0 1 1 1 0 15 1 0 1 1 0 0 49500.0 0 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 -0.7435145661134329 1.8046155180928998 0.08360666740041044 0 1 1 1 0 11 1 0 0 1 1 0 80000.0 0 0.24261127446320968 0.38950061132561975 1.7248787237282124 0.7342974433930187 -0.5541346563708683 2.5283905509019418 0 1 1 1 0 9 1 0 1 1 0 0 50000.0 0 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.7092962137352213 0 1 1 1 0 8 1 0 0 1 1 0 58000.0 0 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 -0.37892001326204144 481 rows X 20 columns Total time taken by feature scaling: 44.45 sec Feature selection using rfe ... feature selected by RFE: ['stories', 'garagepl', 'bathrms', 'airco_0', 'bedrooms_1', 'homestyle', 'fullbase_0', 'prefarea', 'lotsize'] Total time taken by feature selection: 38.33 sec scaling Features of rfe data ... columns that will be scaled: ['r_stories', 'r_garagepl', 'r_bathrms', 'r_homestyle', 'r_prefarea', 'r_lotsize'] Dataset sample after scaling: r_bedrooms_1 r_fullbase_0 id price r_airco_0 r_stories r_garagepl r_bathrms r_homestyle r_prefarea r_lotsize 1 0 10 54500.0 1 -0.9243489557048288 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.9405595540664473 1 0 12 63900.0 0 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 0.5744513081034206 0 0 13 99000.0 0 0.24261127446320968 0.38950061132561975 1.7248787237282124 0.7342974433930187 -0.5541346563708683 1.7638056298068683 1 1 14 48000.0 1 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.48275253341075514 0 1 16 87000.0 0 1.4095715046312483 1.5752588015004494 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 1.5240469014634748 0 0 17 57000.0 0 0.24261127446320968 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.303405453153886 1 1 15 49500.0 1 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 -0.7435145661134329 1.8046155180928998 0.08360666740041044 0 1 11 80000.0 1 0.24261127446320968 0.38950061132561975 1.7248787237282124 0.7342974433930187 -0.5541346563708683 2.5283905509019418 1 1 9 50000.0 1 -0.9243489557048288 0.38950061132561975 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.7092962137352213 0 1 8 58000.0 1 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 -0.37892001326204144 481 rows X 11 columns Total time taken by feature scaling: 36.07 sec scaling Features of pca data ... columns that will be scaled: ['prefarea', 'bathrms', 'stories', 'homestyle', 'lotsize', 'garagepl'] Dataset sample after scaling: recroom_1 airco_0 recroom_0 gashw_0 airco_1 id driveway_1 gashw_1 bedrooms_1 fullbase_0 bedrooms_0 fullbase_1 price driveway_0 prefarea bathrms stories homestyle lotsize garagepl 1 0 0 1 1 285 1 0 0 1 1 0 78000.0 0 1.8046155180928782 -0.5797509043642026 1.409571504631246 0.7342974433930172 0.4517401479276683 -0.7962575788492109 0 1 1 1 0 58 1 0 0 0 1 1 61100.0 0 1.8046155180928782 -0.5797509043642026 0.24261127446320932 0.7342974433930172 -0.822568053897455 1.5752588015004514 0 1 1 1 0 464 1 0 0 1 1 0 69000.0 0 1.8046155180928782 -0.5797509043642026 -0.9243489557048274 0.7342974433930172 0.42342218788711006 1.5752588015004514 0 1 1 1 0 393 1 0 0 0 1 1 86900.0 0 1.8046155180928782 -0.5797509043642026 -0.9243489557048274 0.7342974433930172 2.1130604703070883 1.5752588015004514 0 1 1 1 0 283 1 0 0 1 1 0 58000.0 0 1.8046155180928782 -0.5797509043642026 0.24261127446320932 0.7342974433930172 -0.3883593332755611 1.5752588015004514 0 1 1 1 0 387 1 0 0 0 1 1 47000.0 0 1.8046155180928782 -0.5797509043642026 0.24261127446320932 -0.7435145661134314 -1.4148853847457994 -0.7962575788492109 0 1 1 1 0 144 1 0 1 1 0 0 35000.0 0 -0.5541346563708951 -0.5797509043642026 0.24261127446320932 -0.7435145661134314 -0.1429370129240559 -0.7962575788492109 0 0 1 1 1 411 1 0 0 1 1 0 83000.0 0 -0.5541346563708951 -0.5797509043642026 1.409571504631246 0.7342974433930172 -0.16181565295109474 -0.7962575788492109 0 1 1 1 0 487 1 0 0 1 1 0 38000.0 0 -0.5541346563708951 -0.5797509043642026 -0.9243489557048274 -0.7435145661134314 -1.105747654303038 -0.7962575788492109 0 1 1 1 0 35 1 0 1 1 0 0 45000.0 0 -0.5541346563708951 -0.5797509043642026 -0.9243489557048274 -0.7435145661134314 -0.7989697538636564 -0.7962575788492109 481 rows X 20 columns Total time taken by feature scaling: 44.18 sec Dimension Reduction using pca ... PCA columns: ['col_0', 'col_1', 'col_2', 'col_3', 'col_4', 'col_5', 'col_6', 'col_7', 'col_8', 'col_9'] Total time taken by PCA: 6.88 sec 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Model Training started ... Starting customized hyperparameter update ... Skipping customized hyperparameter tuning Hyperparameters used for model training: response_column : price name : xgboost model_type : Regression column_sampling : (1, 0.6) min_impurity : (0.0, 0.1, 0.2, 0.3) lambda1 : (0.01, 0.1, 1, 10) 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) seed : 42 Total number of models for xgboost : 10240 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- 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, 20, 30, 40) seed : 42 Total number of models for decision_forest : 320 -------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------- Performing hyperparameter tuning ... xgboost ---------------------------------------------------------------------------------------------------- decision_forest ---------------------------------------------------------------------------------------------------- Leaderboard RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_1 rfe 8427.249621 1.394711e+08 0.024186 12.256896 -0.226853 11809.788825 0.155517 44860.075880 0.831416 0.835116 1668.854740 0.024415 0.828195 1 2 XGBOOST_0 lasso 8233.900404 1.412864e+08 0.024208 12.021986 0.890327 11886.393576 0.155589 49616.394501 0.829222 0.836897 1676.436224 0.024553 0.822569 2 3 XGBOOST_3 lasso 8233.900404 1.412864e+08 0.024208 12.021986 0.890327 11886.393576 0.155589 49616.394501 0.829222 0.836897 1676.436224 0.024553 0.822569 3 4 XGBOOST_2 pca 8762.454560 1.721874e+08 0.030721 13.421626 -2.003179 13122.018442 0.175275 60568.021287 0.791871 0.794088 2107.466538 0.030929 0.787443 4 5 DECISIONFOREST_1 rfe 9683.028747 2.273851e+08 0.032219 13.522362 0.032294 15079.293192 0.179496 71500.000000 0.725152 0.732519 2515.412163 0.032992 0.719900 5 6 DECISIONFOREST_0 lasso 9844.712596 2.469670e+08 0.033416 13.614288 0.124220 15715.184984 0.182801 71500.000000 0.701482 0.709846 2686.399136 0.034488 0.689852 6 7 DECISIONFOREST_3 lasso 10262.567624 2.878648e+08 0.036579 13.965556 -0.144662 16966.577888 0.191257 71945.000000 0.652048 0.668895 3111.956207 0.038856 0.638491 7 8 DECISIONFOREST_2 pca 11664.438993 3.793982e+08 0.056060 16.069311 1.031014 19478.147399 0.236771 84146.052632 0.541408 0.563668 4628.203132 0.064916 0.531651 8 rows X 16 columns 1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation Completed: ｜⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿｜ 100% - 19/19 - Display model leaderboard.
>>> aml.leaderboard()
RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_1 rfe 8427.249621 1.394711e+08 0.024186 12.256896 -0.226853 11809.788825 0.155517 44860.075880 0.831416 0.835116 1668.854740 0.024415 0.828195 1 2 XGBOOST_0 lasso 8233.900404 1.412864e+08 0.024208 12.021986 0.890327 11886.393576 0.155589 49616.394501 0.829222 0.836897 1676.436224 0.024553 0.822569 2 3 XGBOOST_3 lasso 8233.900404 1.412864e+08 0.024208 12.021986 0.890327 11886.393576 0.155589 49616.394501 0.829222 0.836897 1676.436224 0.024553 0.822569 3 4 XGBOOST_2 pca 8762.454560 1.721874e+08 0.030721 13.421626 -2.003179 13122.018442 0.175275 60568.021287 0.791871 0.794088 2107.466538 0.030929 0.787443 4 5 DECISIONFOREST_1 rfe 9683.028747 2.273851e+08 0.032219 13.522362 0.032294 15079.293192 0.179496 71500.000000 0.725152 0.732519 2515.412163 0.032992 0.719900 5 6 DECISIONFOREST_0 lasso 9844.712596 2.469670e+08 0.033416 13.614288 0.124220 15715.184984 0.182801 71500.000000 0.701482 0.709846 2686.399136 0.034488 0.689852 6 7 DECISIONFOREST_3 lasso 10262.567624 2.878648e+08 0.036579 13.965556 -0.144662 16966.577888 0.191257 71945.000000 0.652048 0.668895 3111.956207 0.038856 0.638491 7 8 DECISIONFOREST_2 pca 11664.438993 3.793982e+08 0.056060 16.069311 1.031014 19478.147399 0.236771 84146.052632 0.541408 0.563668 4628.203132 0.064916 0.531651
- Display the best performing model.
>>> aml.leader()
RANK MODEL_ID FEATURE_SELECTION MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD ADJUSTED_R2 0 1 XGBOOST_1 rfe 8427.249621 1.394711e+08 0.024186 12.256896 -0.226853 11809.788825 0.155517 44860.07588 0.831416 0.835116 1668.85474 0.024415 0.828195
- Display hyperparameters for trained model.
- Display model hyperparameters for rank 2.
>>> aml.model_hyperparameters(rank=2)
{'response_column': 'price', 'name': 'xgboost', 'model_type': 'Regression', 'column_sampling': 1, 'min_impurity': 0.0, 'lambda1': 0.01, 'shrinkage_factor': 0.5, 'max_depth': 5, 'min_node_size': 1, 'iter_num': 10, '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': 0.01, 'shrinkage_factor': 0.5, 'max_depth': 5, 'min_node_size': 1, 'iter_num': 20, 'seed': 42, 'persist': False}
- Display model hyperparameters for rank 2.
- Generate prediction on test dataset using best performing model.
>>> prediction = aml.predict(housing_test)
Data Transformation started ... Performing transformation carried out in feature engineering phase ... Updated dataset after performing customized variable width bin-code transformation : bathrms stories gashw airco sn fullbase id homestyle lotsize garagepl prefarea driveway recroom price bedrooms 1 1 no no 443 no 51 Eclectic 3520.0 0 yes yes no 65000.0 big_house 1 2 no no 469 no 8 Eclectic 2176.0 0 yes yes yes 55000.0 small_house 1 2 no yes 440 no 19 Eclectic 6862.0 2 yes yes no 69000.0 big_house 1 1 no yes 401 yes 40 Eclectic 7410.0 2 yes yes yes 92500.0 big_house 1 1 no no 25 no 48 Classic 4960.0 0 no yes no 42000.0 small_house 1 1 no no 251 yes 14 Classic 3450.0 2 no yes no 48500.0 big_house 1 1 no yes 16 no 23 Classic 3185.0 0 no yes no 37900.0 small_house 1 1 no no 301 no 9 Eclectic 4080.0 0 no yes no 55000.0 small_house 1 1 no no 411 yes 33 Eclectic 9000.0 1 yes yes no 90000.0 big_house 1 2 no yes 161 no 49 Eclectic 3162.0 1 no yes no 63900.0 big_house 46 rows X 15 columns Updated dataset after performing customized categorical encoding : prefarea bathrms stories gashw airco sn fullbase id homestyle lotsize bedrooms garagepl driveway recroom price 62906.33597883598 1 1 no yes 53 yes 11 2 9166.0 small_house 2 yes no 68000.0 62906.33597883598 1 2 no no 140 no 43 1 3750.0 big_house 0 yes no 43000.0 62906.33597883598 1 2 no no 255 no 15 2 4360.0 big_house 0 yes no 61000.0 62906.33597883598 1 2 no no 364 yes 16 2 10700.0 big_house 0 yes yes 72000.0 62906.33597883598 1 2 yes no 117 no 37 2 3760.0 big_house 2 yes no 93000.0 62906.33597883598 1 2 no no 237 no 53 1 3630.0 big_house 3 yes no 43000.0 83851.72413793103 1 1 no yes 401 yes 40 2 7410.0 big_house 2 yes yes 92500.0 83851.72413793103 1 1 no no 411 yes 33 2 9000.0 big_house 1 yes no 90000.0 83851.72413793103 1 2 no no 463 yes 13 1 2610.0 big_house 0 yes no 49000.0 83851.72413793103 1 3 no no 408 yes 22 2 6420.0 big_house 0 yes no 87500.0 46 rows X 15 columns Updated dataset after performing categorical encoding : prefarea bathrms stories gashw_0 gashw_1 airco_0 airco_1 sn fullbase_0 fullbase_1 id homestyle lotsize bedrooms_0 bedrooms_1 garagepl driveway_0 driveway_1 recroom_0 recroom_1 price 62906.33597883598 1 2 1 0 1 0 255 1 0 15 2 4360.0 1 0 0 0 1 1 0 61000.0 62906.33597883598 1 2 0 1 1 0 117 1 0 37 2 3760.0 1 0 2 0 1 1 0 93000.0 62906.33597883598 1 2 1 0 1 0 237 1 0 53 1 3630.0 1 0 3 0 1 1 0 43000.0 62906.33597883598 1 2 1 0 1 0 13 1 0 17 1 1700.0 1 0 0 0 1 1 0 27000.0 62906.33597883598 1 2 0 1 1 0 198 1 0 20 1 4350.0 1 0 1 1 0 1 0 40500.0 62906.33597883598 1 1 1 0 1 0 234 1 0 36 1 3970.0 0 1 0 1 0 1 0 32500.0 83851.72413793103 1 2 1 0 1 0 463 0 1 13 1 2610.0 1 0 0 0 1 1 0 49000.0 83851.72413793103 1 1 1 0 1 0 441 1 0 39 2 3520.0 1 0 2 0 1 1 0 51900.0 83851.72413793103 1 2 1 0 1 0 469 1 0 8 2 2176.0 0 1 0 0 1 0 1 55000.0 83851.72413793103 1 1 1 0 1 0 472 0 1 27 2 2787.0 1 0 0 0 1 1 0 60500.0 46 rows X 21 columns Updated dataset after performing customized anti-selection : prefarea bathrms stories gashw_0 gashw_1 airco_0 airco_1 fullbase_0 fullbase_1 id homestyle lotsize bedrooms_0 bedrooms_1 garagepl driveway_0 driveway_1 recroom_0 recroom_1 price 62906.33597883598 1 2 1 0 1 0 1 0 15 2 4360.0 1 0 0 0 1 1 0 61000.0 62906.33597883598 1 2 0 1 1 0 1 0 37 2 3760.0 1 0 2 0 1 1 0 93000.0 62906.33597883598 1 2 1 0 1 0 1 0 53 1 3630.0 1 0 3 0 1 1 0 43000.0 62906.33597883598 1 2 1 0 1 0 1 0 17 1 1700.0 1 0 0 0 1 1 0 27000.0 62906.33597883598 1 2 0 1 1 0 1 0 20 1 4350.0 1 0 1 1 0 1 0 40500.0 62906.33597883598 1 1 1 0 1 0 1 0 36 1 3970.0 0 1 0 1 0 1 0 32500.0 83851.72413793103 1 2 1 0 1 0 0 1 13 1 2610.0 1 0 0 0 1 1 0 49000.0 83851.72413793103 1 1 1 0 1 0 1 0 39 2 3520.0 1 0 2 0 1 1 0 51900.0 83851.72413793103 1 2 1 0 1 0 1 0 8 2 2176.0 0 1 0 0 1 0 1 55000.0 83851.72413793103 1 1 1 0 1 0 0 1 27 2 2787.0 1 0 0 0 1 1 0 60500.0 46 rows X 20 columns Performing transformation carried out in data preparation phase ... Updated dataset after performing Lasso feature selection: id recroom_1 stories gashw_0 bedrooms_0 garagepl fullbase_1 bathrms airco_0 recroom_0 airco_1 driveway_1 gashw_1 bedrooms_1 homestyle fullbase_0 driveway_0 prefarea lotsize price 24 1 1 1 0 0 1 2 1 0 0 1 0 1 2 0 0 62906.336 4100.0 64900.0 32 1 1 1 1 0 1 1 0 0 1 1 0 0 2 0 0 83851.7241 6825.0 77500.0 8 1 2 1 0 0 0 1 1 0 0 1 0 1 2 1 0 83851.7241 2176.0 55000.0 27 0 1 1 1 0 1 1 1 1 0 1 0 0 2 0 0 83851.7241 2787.0 60500.0 21 0 1 1 1 0 0 1 1 1 0 1 0 0 1 1 0 83851.7241 2398.0 44555.0 22 0 3 1 1 0 1 1 1 1 0 1 0 0 2 0 0 83851.7241 6420.0 87500.0 11 0 1 1 0 2 1 1 0 1 1 1 0 1 2 0 0 62906.336 9166.0 68000.0 43 0 2 1 1 0 0 1 1 1 0 1 0 0 1 1 0 62906.336 3750.0 43000.0 15 0 2 1 1 0 0 1 1 1 0 1 0 0 2 1 0 62906.336 4360.0 61000.0 37 0 2 0 1 2 0 1 1 1 0 1 1 0 2 1 0 62906.336 3760.0 93000.0 46 rows X 20 columns Updated dataset after performing scaling on Lasso selected features : recroom_1 airco_0 recroom_0 gashw_0 airco_1 id driveway_1 gashw_1 bedrooms_1 fullbase_0 bedrooms_0 fullbase_1 price driveway_0 stories garagepl bathrms homestyle prefarea lotsize 0 1 1 1 0 21 1 0 0 1 1 0 44555.0 0 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 1.8046155180928998 -1.2954779865747776 0 0 1 1 1 11 1 0 1 0 0 1 68000.0 0 -0.9243489557048288 1.5752588015004494 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 1.898787906000196 0 1 1 1 0 43 1 0 0 1 1 0 43000.0 0 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.6573799536608644 0 1 1 1 0 15 1 0 0 1 1 0 61000.0 0 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 -0.369480693248522 0 1 1 1 0 53 1 0 0 1 1 0 43000.0 0 0.24261127446320968 2.761016991675279 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.714015873741981 0 1 1 1 0 17 1 0 0 1 1 0 27000.0 0 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -1.6249102550466057 1 0 0 1 1 40 1 0 0 0 1 1 92500.0 0 -0.9243489557048288 1.5752588015004494 -0.5797509043642046 0.7342974433930187 1.8046155180928998 1.0700156088131905 1 1 0 1 0 35 0 0 0 0 1 1 78500.0 1 0.24261127446320968 0.38950061132561975 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -1.0977242322915457 1 1 0 1 0 24 1 0 1 0 0 1 64900.0 0 -0.9243489557048288 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.4921918534242745 1 1 0 1 0 16 1 0 0 0 1 1 72000.0 0 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 2.622783751037136 46 rows X 20 columns Updated dataset after performing RFE feature selection: id stories garagepl bathrms airco_0 bedrooms_1 homestyle fullbase_0 prefarea lotsize price 24 1 0 2 1 1 2 0 62906.336 4100.0 64900.0 32 1 0 1 0 0 2 0 83851.7241 6825.0 77500.0 8 2 0 1 1 1 2 1 83851.7241 2176.0 55000.0 27 1 0 1 1 0 2 0 83851.7241 2787.0 60500.0 21 1 0 1 1 0 1 1 83851.7241 2398.0 44555.0 22 3 0 1 1 0 2 0 83851.7241 6420.0 87500.0 11 1 2 1 0 1 2 0 62906.336 9166.0 68000.0 43 2 0 1 1 0 1 1 62906.336 3750.0 43000.0 15 2 0 1 1 0 2 1 62906.336 4360.0 61000.0 37 2 2 1 1 0 2 1 62906.336 3760.0 93000.0 46 rows X 11 columns Updated dataset after performing scaling on RFE selected features : r_bedrooms_1 r_fullbase_0 id price r_airco_0 r_stories r_garagepl r_bathrms r_homestyle r_prefarea r_lotsize 1 0 24 64900.0 1 -0.9243489557048288 -0.7962575788492099 1.7248787237282124 0.7342974433930187 -0.5541346563708683 -0.4921918534242745 0 0 32 77500.0 0 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 1.8046155180928998 0.7939154984177472 1 1 8 55000.0 1 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 1.8046155180928998 -1.4002544387248432 0 0 27 60500.0 1 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 1.8046155180928998 -1.1118832123118247 0 1 21 44555.0 1 -0.9243489557048288 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 1.8046155180928998 -1.2954779865747776 0 0 22 87500.0 1 1.4095715046312483 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 1.8046155180928998 0.6027692681439788 1 0 11 68000.0 0 -0.9243489557048288 1.5752588015004494 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 1.898787906000196 0 1 43 43000.0 1 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 -0.7435145661134329 -0.5541346563708683 -0.6573799536608644 0 1 15 61000.0 1 0.24261127446320968 -0.7962575788492099 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 -0.369480693248522 0 1 37 93000.0 1 0.24261127446320968 1.5752588015004494 -0.5797509043642046 0.7342974433930187 -0.5541346563708683 -0.6526602936541048 46 rows X 11 columns Updated dataset after performing scaling for PCA feature selection : recroom_1 airco_0 recroom_0 gashw_0 airco_1 id driveway_1 gashw_1 bedrooms_1 fullbase_0 bedrooms_0 fullbase_1 price driveway_0 prefarea bathrms stories homestyle lotsize garagepl 1 1 0 1 0 24 1 0 1 0 0 1 64900.0 0 -0.5541346539875237 1.7248787237282066 -0.9243489557048274 0.7342974433930172 -0.49219185342427485 -0.7962575788492109 1 0 0 1 1 32 1 0 0 0 1 1 77500.0 0 1.804615513821303 -0.5797509043642026 -0.9243489557048274 0.7342974433930172 0.7939154984177478 -0.7962575788492109 1 1 0 1 0 8 1 0 1 1 0 0 55000.0 0 1.804615513821303 -0.5797509043642026 0.24261127446320932 0.7342974433930172 -1.4002544387248441 -0.7962575788492109 0 1 1 1 0 27 1 0 0 0 1 1 60500.0 0 1.804615513821303 -0.5797509043642026 -0.9243489557048274 0.7342974433930172 -1.1118832123118256 -0.7962575788492109 0 1 1 1 0 21 1 0 0 1 1 0 44555.0 0 1.804615513821303 -0.5797509043642026 -0.9243489557048274 -0.7435145661134314 -1.2954779865747785 -0.7962575788492109 0 1 1 1 0 22 1 0 0 0 1 1 87500.0 0 1.804615513821303 -0.5797509043642026 1.409571504631246 0.7342974433930172 0.6027692681439792 -0.7962575788492109 0 0 1 1 1 11 1 0 1 0 0 1 68000.0 0 -0.5541346539875237 -0.5797509043642026 -0.9243489557048274 0.7342974433930172 1.8987879060001973 1.5752588015004514 0 1 1 1 0 43 1 0 0 1 1 0 43000.0 0 -0.5541346539875237 -0.5797509043642026 0.24261127446320932 -0.7435145661134314 -0.6573799536608649 -0.7962575788492109 0 1 1 1 0 15 1 0 0 1 1 0 61000.0 0 -0.5541346539875237 -0.5797509043642026 0.24261127446320932 0.7342974433930172 -0.36948069324852223 -0.7962575788492109 0 1 1 0 0 37 1 1 0 1 1 0 93000.0 0 -0.5541346539875237 -0.5797509043642026 0.24261127446320932 0.7342974433930172 -0.6526602936541052 1.5752588015004514 46 rows X 20 columns Updated dataset after performing PCA feature selection : id col_0 col_1 col_2 col_3 col_4 col_5 col_6 col_7 col_8 col_9 price 0 40 1.517005 2.635827 -0.254247 0.423657 -0.071790 -0.699173 1.123907 0.539062 -0.023292 0.589272 92500.0 1 27 -0.907076 1.115731 -1.753948 1.240695 0.341903 -0.906526 -0.000200 -0.165580 -0.384095 -0.464056 60500.0 2 35 0.199440 -0.655693 -0.973420 -1.287750 1.822717 -0.705320 0.523943 -0.663558 -0.058868 1.033697 78500.0 3 51 -0.831862 0.983331 -1.367625 1.188594 -0.213138 -0.572543 -0.901387 0.190808 -0.489312 0.225768 65000.0 4 24 -0.537790 0.121912 -0.965847 -0.607395 2.236753 0.806179 0.160764 -0.209506 1.265151 0.373994 64900.0 5 21 -0.851571 0.134084 -0.639709 2.122880 0.145022 -0.935130 -0.694137 0.133201 -0.479635 0.151287 44555.0 6 16 0.634760 1.141951 -0.301314 -0.451965 -0.887044 2.253530 1.215956 -1.351005 0.170636 0.298380 72000.0 7 22 0.788204 0.450834 -1.910249 0.856887 -1.306560 -0.058988 0.067773 -0.863751 0.045674 -0.688628 87500.0 8 32 0.441422 1.783878 -1.511197 1.200858 -0.102227 0.551105 1.190928 0.583395 -0.100689 0.490966 77500.0 9 11 0.665806 2.229285 1.326236 -0.984332 -0.103284 0.660512 0.754063 0.745793 0.443762 -0.770841 68000.0 10 rows X 12 columns Data Transformation completed.⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿｜ 100% - 15/15 Following model is being picked for evaluation: Model ID : XGBOOST_1 Feature Selection Method : rfe Prediction : id Prediction Confidence_Lower Confidence_upper price 0 24 60493.951359 60493.951359 60493.951359 64900.0 1 32 79532.256218 79532.256218 79532.256218 77500.0 2 8 55241.854455 55241.854455 55241.854455 55000.0 3 27 60479.372933 60479.372933 60479.372933 60500.0 4 21 37397.395472 37397.395472 37397.395472 44555.0 5 22 88903.303042 88903.303042 88903.303042 87500.0 6 11 71834.288947 71834.288947 71834.288947 68000.0 7 43 42734.457781 42734.457781 42734.457781 43000.0 8 15 61650.265369 61650.265369 61650.265369 61000.0 9 37 64685.898547 64685.898547 64685.898547 93000.0
>>> prediction.head()
id Prediction Confidence_Lower Confidence_upper price 10 53895.959449 53895.959449 53895.959449 41000.0 12 77403.76960199999 77403.76960199999 77403.76960199999 67000.0 13 45378.98779 45378.98779 45378.98779 49000.0 14 45164.636739999994 45164.636739999994 45164.636739999994 48500.0 16 72975.583423 72975.583423 72975.583423 72000.0 17 39109.471668 39109.471668 39109.471668 27000.0 15 61650.265369 61650.265369 61650.265369 61000.0 11 71834.288947 71834.288947 71834.288947 68000.0 9 55931.963123 55931.963123 55931.963123 55000.0 8 55241.854455 55241.854455 55241.854455 55000.0
- Generate evaluation metrics on test dataset using best performing model.
>>> performance_metrics = aml.evaluate(housing_test)
Skipping data transformation as data is already transformed. Following model is being picked for evaluation: Model ID : XGBOOST_1 Feature Selection Method : rfe Performance Metrics : MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD 0 6612.872179 7.447203e+07 0.027196 12.880711 -4.603534 8629.718038 0.164913 28314.101453 0.768711 0.771862 1331.165781 0.026174>>> performance_metrics
MAE MSE MSLE MAPE MPE RMSE RMSLE ME R2 EV MPD MGD 6612.872178891304 74472033.41870987 0.02719635873394214 12.88071111950302 -4.603533619606152 8629.718038192781 0.1649131854459859 28314.101452999996 0.768710810641775 0.7718620121604588 1331.1657809502685 0.0261739261346348