AutoRegressor for regression with early stopping condition and customization - Example 2: Run AutoRegressor for Regression Problem with Early Stopping Condition and Customization - Teradata Package for Python

Teradata® Package for Python User Guide

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VantageCloud
VantageCore
Edition
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IntelliFlex
VMware
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Teradata Package for Python
Release Number
20.00
Published
March 2024
Language
English (United States)
Last Update
2024-04-09
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Teradata Vantage

This example predict predict the price of house based on different factors.

Run AutoRegressor to get the best performing model with following specifications:

  • Set early stopping criteria, that is, time limit to 300 sec and performance metrics R2 threshold value to 0.7.
  • 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.
  1. 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")
  2. 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:  5
    
    Enter the label for bin 2 of feature bedrooms:  big_house
    
    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
    
    Customization of categorical encoding has been completed successfully.
    
    Customizing Antiselect Features ...
    
    Enter the feature or list of features for antiselect:  sn
    
    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 Train Test Split
    
    Index 2: Customize Data Imbalance Handling
    
    Index 3: Customize Outlier Handling
    
    Index 4: Customize Feature Scaling
    
    Index 5: Back to main menu
    
    Index 6: Generate custom json and exit
    --------------------------------------------------------------------------------
    
    Enter the list of indices you want to customize in data preparation phase:  1,2,3,4,5
    
    Customizing Train Test Split ...
    
    Enter the train size for train test split:  0.75
    
    Customization of train test split has been completed successfully.
    
    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:  0.1
    
    Enter the upper percentile value for outlier handling:  0.9
    
    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
    
    Customization of outlier handling has been completed successfully.
    
    Available feature scaling methods with corresponding indices:
    Index 1: maxabs
    Index 2: mean
    Index 3: midrange
    Index 4: range
    Index 5: rescale
    Index 6: std
    Index 7: sum
    Index 8: ustd
    
    Enter the corresponding index feature scaling method:  6
    
    Customization of feature scaling has been completed successfully.
    
    Customization of data preparation 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:  3
    
    Customizing Model Training Phase ...
    
    Available options for customization of model training phase with corresponding indices: 
    --------------------------------------------------------------------------------
    
    Index 1: Customize Model Hyperparameter
    
    Index 2: Back to main menu
    
    Index 3: Generate custom json and exit
    --------------------------------------------------------------------------------
    
    Enter the list of indices you want to customize in model training phase:  1
    
    Customizing Model Hyperparameter ...
    
    Available models for hyperparameter tuning with corresponding indices:
    Index 1: decision_forest
    Index 2: xgboost
    Index 3: knn
    Index 4: glm
    Index 5: svm
    
    Available hyperparamters update methods with corresponding indices:
    Index 1: ADD
    Index 2: REPLACE
    
    Enter the list of model indices for performing hyperparameter tuning:  2
    
    Available hyperparameters for model 'xgboost' with corresponding indices:
    Index 1: min_impurity
    Index 2: max_depth
    Index 3: min_node_size
    Index 4: shrinkage_factor
    Index 5: iter_num
    
    Enter the list of hyperparameter indices for model 'xgboost':  3
    
    Enter the index of corresponding update method for hyperparameters 'min_node_size' for model 'xgboost':  1
    
    Enter the list of value for hyperparameter 'min_node_size' for model 'xgboost':  1,2
    
    Customization of model hyperparameter has been completed successfully.
    
    Available options for customization of model training phase with corresponding indices: 
    --------------------------------------------------------------------------------
    
    Index 1: Customize Model Hyperparameter
    
    Index 2: Back to main menu
    
    Index 3: Generate custom json and exit
    --------------------------------------------------------------------------------
    
    Enter the list of indices you want to customize in model training phase:  2
    
    Customization of model training 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:  4
    
    Generating custom json and exiting ...
    
    Process of generating custom config file for AutoML has been completed successfully.
    
    'custom_housing.json' file is generated successfully under the current working directory. 
  3. Create an AutoRegressor instance.
    >>> aml = AutoRegressor(exclude=['glm','svm','knn'],
                            verbose=2,
                            max_runtime_secs=300,
                            stopping_metric='R2',
                            stopping_tolerance=0.7,
                            custom_config_file='custom_housing.json')
  4. 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": 5,
                    "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": [
            "sn"
        ],
        "TrainTestSplitIndicator": true,
        "TrainingSize": 0.75,
        "DataImbalanceIndicator": true,
        "DataImbalanceMethod": "SMOTE",
        "OutlierFilterIndicator": true,
        "OutlierFilterMethod": "percentile",
        "OutlierLowerPercentile": 0.1,
        "OutlierUpperPercentile": 0.9,
        "OutlierFilterParam": {
            "bathrms": {
                "replacement_value": "delete"
            }
        },
        "FeatureScalingIndicator": true,
        "FeatureScalingMethod": "std",
        "HyperparameterTuningIndicator": true,
        "HyperparameterTuningParam": {
            "xgboost": {
                "min_node_size": {
                    "Method": "ADD",
                    "Value": [
                        1,
                        2
                    ]
                }
            }
        }
    }
    
    1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation
    Feature Exploration started ...
    Column Summary:
    ColumnName    Datatype    NonNullCount    NullCount    BlankCount    ZeroCount    PositiveCount    NegativeCount    NullPercentage    NonNullPercentage
    driveway    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
    bathrms    INTEGER    492    0    None    0    492    0    0.0    100.0
    lotsize    FLOAT    492    0    None    0    492    0    0.0    100.0
    homestyle    VARCHAR(20) CHARACTER SET LATIN    492    0    0    None    None    None    0.0    100.0
    sn    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
    stories    INTEGER    492    0    None    0    492    0    0.0    100.0
    bedrooms    INTEGER    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
    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   garagepl           2.235772
    2    bathrms           0.203252
    3   bedrooms           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 ...
                                                                                            
    Updated dataset after removing duplicate records:
    sn    price    lotsize    bedrooms    bathrms    stories    driveway    recroom    fullbase    gashw    airco    garagepl    prefarea    homestyle
    183    58000.0    4340.0    3    1    1    yes    no    no    no    no    0    no    Eclectic
    265    50000.0    3640.0    2    1    1    yes    no    no    no    no    1    no    Classic
    101    57000.0    4500.0    3    2    2    no    no    yes    no    yes    0    no    Eclectic
    427    49500.0    5320.0    2    1    1    yes    no    no    no    no    1    yes    Classic
    122    80000.0    10500.0    4    2    2    yes    no    no    no    no    1    no    Eclectic
    387    83900.0    11460.0    3    1    3    yes    no    no    no    no    2    yes    Eclectic
    326    99000.0    8880.0    3    2    2    yes    no    yes    no    yes    1    no    Eclectic
    305    60000.0    5800.0    3    1    1    yes    no    no    yes    no    2    no    Eclectic
    80    63900.0    6360.0    2    1    1    yes    no    yes    no    yes    1    no    Eclectic
    345    88000.0    4500.0    3    1    4    yes    no    no    no    yes    0    no    Eclectic
                                                                                            
    Handling less significant features from data ...
    All categorical columns seem to be significant.                                         
                                                                                            
    Total time to handle less significant features: 15.04 sec
                                                                                             
    Handling Date Features ...
    Dataset does not contain any feature related to dates.                                   
                                                                                             
    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 ...
    No Missing Value Detected.                                                               
                                                                                             
    Total time to find missing values in data: 6.60 sec
                                                                                             
    Imputing Missing Values ...
    No imputation is Required.                                                               
                                                                                             
    Time taken to perform imputation: 0.00 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    5    big_house
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710268620784148"'0
                                                                                             
    Updated dataset after performing Variable-Width binning:
    homestyle    gashw    lotsize    stories    recroom    sn    price    bathrms    driveway    prefarea    id    fullbase    airco    garagepl    bedrooms
    bungalow    no    6000.0    4    yes    522    105000.0    2    yes    no    104    no    yes    1    big_house
    bungalow    no    9960.0    2    no    363    175000.0    2    yes    yes    296    yes    no    2    big_house
    bungalow    no    6000.0    2    no    546    105000.0    1    yes    no    304    no    yes    1    big_house
    bungalow    no    6600.0    2    yes    361    130000.0    2    yes    yes    328    yes    yes    1    big_house
    bungalow    no    7420.0    3    no    378    190000.0    2    yes    yes    384    no    yes    2    big_house
    bungalow    no    4600.0    2    yes    130    127000.0    2    yes    no    432    no    yes    2    big_house
    Eclectic    no    3000.0    2    no    242    52000.0    1    yes    no    28    no    yes    0    small_house
    Eclectic    no    3840.0    2    no    425    65500.0    1    yes    yes    44    no    no    1    big_house
    Eclectic    no    4000.0    2    no    118    94500.0    2    yes    no    52    yes    yes    1    big_house
    Eclectic    no    8150.0    1    yes    505    71500.0    2    yes    no    68    yes    no    0    big_house
    Skipping customized string manipulation.⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾⫾| 25% - 5/20
                                                                                             
    Starting Customized Categorical Feature Encoding ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710266975076237"'0
                                                                                             
    Updated dataset after performing ordinal encoding:
    gashw    lotsize    stories    recroom    sn    price    bathrms    driveway    prefarea    id    fullbase    bedrooms    airco    garagepl    homestyle
    yes    3120.0    2    no    165    52900.0    1    no    no    230    yes    big_house    no    0    2
    yes    4500.0    3    no    148    62000.0    2    yes    no    192    no    big_house    no    1    2
    yes    4260.0    2    no    126    95000.0    2    yes    no    480    no    big_house    no    0    2
    yes    5800.0    1    no    305    60000.0    1    yes    no    21    no    big_house    no    2    2
    yes    3420.0    2    no    186    54000.0    1    yes    no    229    no    small_house    no    1    2
    yes    6450.0    1    yes    315    76900.0    2    yes    no    317    yes    big_house    no    0    2
    no    4000.0    2    no    118    94500.0    2    yes    no    52    yes    big_house    yes    1    2
    no    3760.0    1    no    135    65000.0    1    yes    no    108    no    big_house    no    2    2
    no    4260.0    2    no    114    75000.0    1    yes    no    116    yes    big_house    yes    0    2
    no    7160.0    1    no    379    84000.0    1    yes    yes    124    yes    big_house    no    2    2
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710267902992673"'0
                                                                                             
    Updated dataset after performing target encoding:
    prefarea    homestyle    gashw    lotsize    stories    recroom    sn    price    bathrms    driveway    id    fullbase    bedrooms    airco    garagepl
    83851.72413793103    2    no    7085.0    1    yes    384    74700.0    1    yes    268    yes    big_house    no    2
    83851.72413793103    2    no    11460.0    3    no    387    83900.0    1    yes    19    no    big_house    no    2
    83851.72413793103    2    no    6420.0    3    no    358    95000.0    2    yes    107    no    big_house    yes    0
    83851.72413793103    2    no    11175.0    1    no    417    100000.0    1    yes    139    yes    big_house    yes    1
    83851.72413793103    1    no    2145.0    2    no    460    47000.0    1    yes    387    yes    big_house    no    0
    83851.72413793103    2    no    5400.0    2    yes    457    79500.0    1    yes    171    yes    big_house    yes    0
    62906.33597883598    2    no    4260.0    2    no    114    75000.0    1    yes    116    yes    big_house    yes    0
    62906.33597883598    2    no    12900.0    1    no    533    70000.0    1    yes    212    no    big_house    no    2
    62906.33597883598    2    no    4046.0    2    no    348    59500.0    1    yes    228    yes    big_house    no    1
    62906.33597883598    2    no    8250.0    3    no    323    93000.0    2    yes    260    no    big_house    yes    0
                                                                                             
    Performing encoding for categorical columns ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710267249707037"'0
                                                                                             
    ONE HOT Encoding these Columns:
    ['gashw', 'recroom', 'driveway', 'fullbase', 'bedrooms', 'airco']
                                                                                             
    Time taken to encode the columns: 10.46 sec
                                                                                             
    Starting customized mathematical transformation ...
    Skipping customized mathematical transformation.                                         
                                                                                             
    Starting customized non-linear transformation ...
    Skipping customized non-linear transformation.                                           
                                                                                             
    Starting customized anti-select columns ...
                                                                                             
    Updated dataset after performing anti-select columns:
    prefarea    homestyle    gashw_0    gashw_1    lotsize    stories    recroom_0    recroom_1    price    bathrms    driveway_0    driveway_1    id    fullbase_0    fullbase_1    bedrooms_0    bedrooms_1    bedrooms_2    airco_0    airco_1    garagepl
    83851.72413793103    2    1    0    6420.0    3    1    0    95000.0    2    0    1    107    1    0    1    0    0    0    1    0
    83851.72413793103    1    1    0    2145.0    2    1    0    47000.0    1    0    1    387    0    1    1    0    0    1    0    0
    83851.72413793103    2    1    0    5400.0    2    0    1    79500.0    1    0    1    171    0    1    1    0    0    0    1    0
    83851.72413793103    2    1    0    11410.0    2    1    0    73000.0    1    0    1    203    1    0    0    0    1    1    0    0
    83851.72413793103    2    1    0    5720.0    2    1    0    72500.0    1    0    1    339    1    0    0    0    1    0    1    0
    83851.72413793103    2    1    0    6600.0    4    1    0    95500.0    2    0    1    363    0    1    0    0    1    1    0    0
    62906.33597883598    2    1    0    4046.0    2    1    0    59500.0    1    0    1    228    0    1    1    0    0    1    0    1
    62906.33597883598    2    1    0    5948.0    2    1    0    70500.0    1    0    1    308    1    0    1    0    0    0    1    0
    62906.33597883598    2    1    0    6000.0    4    1    0    90000.0    2    0    1    324    1    0    1    0    0    1    0    1
    62906.33597883598    2    1    0    5828.0    4    0    1    83000.0    1    0    1    332    1    0    1    0    0    1    0    0
                                                                                              
    
    1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation
                                                                                              
    Data preparation started ...
                                                                                              
    Spliting of dataset into training and testing ...
    Training size : 0.75                                                                      
    Testing size  : 0.25                                                                      
                                                                                              
    Training data
    prefarea    homestyle    gashw_0    gashw_1    lotsize    stories    recroom_0    recroom_1    price    bathrms    driveway_0    driveway_1    id    fullbase_0    fullbase_1    bedrooms_0    bedrooms_1    bedrooms_2    airco_0    airco_1    garagepl
    83851.72413793103    0    1    0    11440.0    2    1    0    104900.0    1    0    1    37    0    1    1    0    0    1    0    1
    83851.72413793103    0    1    0    13200.0    2    1    0    140000.0    1    0    1    42    0    1    1    0    0    0    1    2
    83851.72413793103    2    1    0    3840.0    2    1    0    65500.0    1    0    1    44    1    0    1    0    0    1    0    1
    83851.72413793103    2    1    0    6900.0    1    0    1    86000.0    2    0    1    46    0    1    1    0    0    1    0    0
    83851.72413793103    2    1    0    2856.0    3    1    0    54000.0    1    0    1    54    1    0    1    0    0    1    0    0
    83851.72413793103    2    1    0    3400.0    2    1    0    61100.0    1    0    1    58    0    1    1    0    0    1    0    2
    62906.33597883598    2    1    0    4340.0    1    1    0    58000.0    1    0    1    8    1    0    1    0    0    1    0    0
    62906.33597883598    2    1    0    10500.0    2    1    0    80000.0    2    0    1    11    1    0    1    0    0    1    0    1
    62906.33597883598    2    1    0    6360.0    1    1    0    63900.0    1    0    1    12    0    1    0    0    1    0    1    1
    62906.33597883598    1    1    0    4120.0    2    1    0    48000.0    1    0    1    14    1    0    0    0    1    1    0    0
                                                                                              
    Testing data
    prefarea    homestyle    gashw_0    gashw_1    lotsize    stories    recroom_0    recroom_1    price    bathrms    driveway_0    driveway_1    id    fullbase_0    fullbase_1    bedrooms_0    bedrooms_1    bedrooms_2    airco_0    airco_1    garagepl
    83851.72413793103    0    1    0    4880.0    2    1    0    118500.0    2    0    1    40    1    0    1    0    0    0    1    1
    83851.72413793103    0    1    0    7440.0    1    0    1    106000.0    2    0    1    97    0    1    1    0    0    0    1    0
    83851.72413793103    2    1    0    6420.0    3    1    0    95000.0    2    0    1    107    1    0    1    0    0    0    1    0
    83851.72413793103    2    1    0    5500.0    3    1    0    89000.0    1    0    1    109    1    0    1    0    0    1    0    1
    83851.72413793103    0    1    0    6360.0    4    1    0    112000.0    2    0    1    200    1    0    1    0    0    0    1    0
    83851.72413793103    2    1    0    4990.0    2    0    1    64900.0    2    0    1    231    0    1    1    0    0    1    0    0
    62906.33597883598    1    1    0    3640.0    1    1    0    50000.0    1    0    1    9    1    0    0    0    1    1    0    1
    62906.33597883598    2    1    0    3150.0    1    1    0    54500.0    2    1    0    10    0    1    0    0    1    1    0    0
    62906.33597883598    2    1    0    8880.0    2    1    0    99000.0    2    0    1    13    0    1    1    0    0    0    1    1
    62906.33597883598    2    0    1    13200.0    1    1    0    99000.0    1    0    1    24    0    1    0    0    1    1    0    1
                                                                                              
    Time taken for spliting of data: 7.32 sec
                                                                                              
    Starting customized outlier processing ...
    Columns with outlier percentage :-                                                                                                                                                                                                                                                                                                                                                                                                                                                                                  
      ColumnName  OutlierPercentage
    0         id           9.756098
    1   garagepl           2.235772
    2      price           8.739837
    3    bathrms           2.235772
    4    lotsize           9.552846
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710269524661840"'
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710275868971124"'20
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710267516664323"'
                                                                                              
    Feature selection using lasso ...
                                                                                              
    feature selected by lasso:
    ['gashw_0', 'airco_1', 'bedrooms_0', 'bathrms', 'airco_0', 'gashw_1', 'homestyle', 'fullbase_1', 'bedrooms_2', 'driveway_1', 'stories', 'recroom_0', 'recroom_1', 'garagepl', 'prefarea', 'lotsize']
                                                                                              
    Total time taken by feature selection: 0.84 sec
                                                                                              
    scaling Features of lasso data ...
                                                                                              
    columns that will be scaled:
    ['bathrms', 'homestyle', 'stories', 'garagepl', 'prefarea', 'lotsize']
                                                                                              
    Training dataset after scaling:
    fullbase_1    bedrooms_2    driveway_1    gashw_0    id    airco_1    bedrooms_0    price    airco_0    recroom_0    recroom_1    gashw_1    bathrms    homestyle    stories    garagepl    prefarea    lotsize
    0    0    1    1    54    0    1    54000.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    1.8093671611394033    -1.080747751737597
    1    0    1    1    58    0    1    61100.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    0.25392610991188536    1.5644560502190725    1.8093671611394033    -0.823995864805428
    0    1    1    0    172    0    0    53000.0    1    1    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    -0.5526794237661611    -0.737153314813665
    0    1    1    1    61    0    0    51500.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    1.8093671611394033    -0.540813636571418
    1    0    1    1    81    1    1    95000.0    0    0    1    0    -0.5698449326198072    0.7474571831400922    -0.899991275637062    1.5644560502190725    1.8093671611394033    0.2615360100916099
    0    1    1    0    236    0    0    56000.0    1    1    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    0.3943732959927245    -0.5526794237661611    -0.8759126066483298
    0    1    1    0    229    0    0    54000.0    1    1    0    1    -0.5698449326198072    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    -0.8145564571976276
    0    0    1    0    157    0    1    87000.0    1    0    1    1    1.7548633720450872    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    0.693388908148475
    0    0    1    0    359    1    1    52000.0    0    1    0    1    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    1.8093671611394033    -1.3549625427441965
    0    0    1    1    44    0    1    65500.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    0.25392610991188536    0.3943732959927245    1.8093671611394033    -0.6163288974338207
                                                                                              
    Testing dataset after scaling:
    fullbase_1    bedrooms_2    driveway_1    gashw_0    id    airco_1    bedrooms_0    price    airco_0    recroom_0    recroom_1    gashw_1    bathrms    homestyle    stories    garagepl    prefarea    lotsize
    0    0    1    1    36    0    1    75000.0    1    0    1    0    1.7548633720450872    0.7474571831400922    0.25392610991188536    1.5644560502190725    -0.5526794237661611    2.1966145696906776
    0    0    1    1    38    0    1    56000.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    0.25392610991188536    -0.7757094582336234    -0.5526794237661611    -1.0127840169614346
    1    0    1    0    494    0    1    71000.0    1    0    1    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    -0.5526794237661611    1.1988691855461826
    1    0    0    1    47    1    1    74500.0    0    1    0    0    1.7548633720450872    0.7474571831400922    -0.899991275637062    1.5644560502190725    -0.5526794237661611    -0.3048284463764098
    0    0    1    1    72    0    1    70000.0    1    1    0    0    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    -0.5526794237661611    -1.026943128373135
    1    1    1    0    24    0    0    99000.0    1    1    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    0.3943732959927245    -0.5526794237661611    3.8013138630167336
    0    0    1    0    192    0    1    62000.0    1    1    0    1    1.7548633720450872    0.7474571831400922    1.4078434954608328    0.3943732959927245    -0.5526794237661611    -0.3048284463764098
    1    0    1    0    566    0    1    125000.0    1    1    0    1    -0.5698449326198072    -2.25072302632687    0.25392610991188536    1.5644560502190725    -0.5526794237661611    -0.38978311484661277
    1    0    1    0    425    0    1    120000.0    1    1    0    1    1.7548633720450872    -2.25072302632687    0.25392610991188536    1.5644560502190725    -0.5526794237661611    1.323469365969147
    1    0    1    1    13    1    1    99000.0    0    1    0    0    1.7548633720450872    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    1.7624018197318623
                                                                                              
    Total time taken by feature scaling: 43.91 sec
                                                                                              
    Feature selection using rfe ...
                                                                                              
    feature selected by RFE:
    ['gashw_0', 'airco_1', 'bedrooms_0', 'bathrms', 'airco_0', 'gashw_1', 'homestyle', 'fullbase_1', 'bedrooms_2', 'fullbase_0', 'driveway_0', 'stories', 'recroom_0', 'recroom_1', 'garagepl', 'prefarea', 'lotsize']
                                                                                              
    Total time taken by feature selection: 32.04 sec
                                                                                              
    scaling Features of rfe data ...
                                                                                              
    columns that will be scaled:
    ['r_bathrms', 'r_homestyle', 'r_stories', 'r_garagepl', 'r_prefarea', 'r_lotsize']
                                                                                              
    Training dataset after scaling:
    r_bedrooms_2    r_airco_1    r_bedrooms_0    id    price    r_recroom_0    r_gashw_1    r_airco_0    r_driveway_0    r_recroom_1    r_gashw_0    r_fullbase_1    r_fullbase_0    r_bathrms    r_homestyle    r_stories    r_garagepl    r_prefarea    r_lotsize
    0    0    1    54    54000.0    1    0    1    0    0    1    0    1    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    1.8093671611394033    -1.080747751737597
    0    0    1    58    61100.0    1    0    1    0    0    1    1    0    -0.5698449326198072    0.7474571831400922    0.25392610991188536    1.5644560502190725    1.8093671611394033    -0.823995864805428
    1    0    0    172    53000.0    1    1    1    0    0    0    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    -0.5526794237661611    -0.737153314813665
    1    0    0    61    51500.0    1    0    1    0    0    1    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    1.8093671611394033    -0.540813636571418
    0    1    1    81    95000.0    0    0    0    0    1    1    1    0    -0.5698449326198072    0.7474571831400922    -0.899991275637062    1.5644560502190725    1.8093671611394033    0.2615360100916099
    1    0    0    236    56000.0    1    1    1    0    0    0    0    1    -0.5698449326198072    0.7474571831400922    -0.899991275637062    0.3943732959927245    -0.5526794237661611    -0.8759126066483298
    1    0    0    229    54000.0    1    1    1    0    0    0    0    1    -0.5698449326198072    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    -0.8145564571976276
    0    0    1    157    87000.0    0    1    1    0    1    0    0    1    1.7548633720450872    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    0.693388908148475
    0    1    1    359    52000.0    1    1    0    0    0    0    0    1    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    1.8093671611394033    -1.3549625427441965
    0    0    1    44    65500.0    1    0    1    0    0    1    0    1    -0.5698449326198072    0.7474571831400922    0.25392610991188536    0.3943732959927245    1.8093671611394033    -0.6163288974338207
                                                                                              
    Testing dataset after scaling:
    r_bedrooms_2    r_airco_1    r_bedrooms_0    id    price    r_recroom_0    r_gashw_1    r_airco_0    r_driveway_0    r_recroom_1    r_gashw_0    r_fullbase_1    r_fullbase_0    r_bathrms    r_homestyle    r_stories    r_garagepl    r_prefarea    r_lotsize
    0    0    1    36    75000.0    0    0    1    0    1    1    0    1    1.7548633720450872    0.7474571831400922    0.25392610991188536    1.5644560502190725    -0.5526794237661611    2.1966145696906776
    0    0    1    38    56000.0    1    0    1    0    0    1    0    1    -0.5698449326198072    0.7474571831400922    0.25392610991188536    -0.7757094582336234    -0.5526794237661611    -1.0127840169614346
    0    0    1    494    71000.0    0    1    1    0    1    0    1    0    -0.5698449326198072    0.7474571831400922    -0.899991275637062    -0.7757094582336234    -0.5526794237661611    1.1988691855461826
    0    1    1    47    74500.0    1    0    0    1    0    1    1    0    1.7548633720450872    0.7474571831400922    -0.899991275637062    1.5644560502190725    -0.5526794237661611    -0.3048284463764098
    0    0    1    72    70000.0    1    0    1    0    0    1    0    1    -0.5698449326198072    0.7474571831400922    1.4078434954608328    -0.7757094582336234    -0.5526794237661611    -1.026943128373135
    1    0    0    24    99000.0    1    1    1    0    0    0    1    0    -0.5698449326198072    0.7474571831400922    -0.899991275637062    0.3943732959927245    -0.5526794237661611    3.8013138630167336
    0    0    1    192    62000.0    1    1    1    0    0    0    0    1    1.7548633720450872    0.7474571831400922    1.4078434954608328    0.3943732959927245    -0.5526794237661611    -0.3048284463764098
    0    0    1    566    125000.0    1    1    1    0    0    0    1    0    -0.5698449326198072    -2.25072302632687    0.25392610991188536    1.5644560502190725    -0.5526794237661611    -0.38978311484661277
    0    0    1    425    120000.0    1    1    1    0    0    0    1    0    1.7548633720450872    -2.25072302632687    0.25392610991188536    1.5644560502190725    -0.5526794237661611    1.323469365969147
    0    1    1    13    99000.0    1    0    0    0    0    1    1    0    1.7548633720450872    0.7474571831400922    0.25392610991188536    0.3943732959927245    -0.5526794237661611    1.7624018197318623
                                                                                              
    Total time taken by feature scaling: 54.84 sec
                                                                                              
    scaling Features of pca data ...
                                                                                              
    columns that will be scaled:
    ['prefarea', 'homestyle', 'lotsize', 'stories', 'bathrms', 'garagepl']
                                                                                              
    Training dataset after scaling:
    fullbase_1    bedrooms_2    fullbase_0    driveway_1    gashw_0    driveway_0    id    airco_1    bedrooms_0    price    airco_0    recroom_0    recroom_1    gashw_1    bedrooms_1    prefarea    homestyle    lotsize    stories    bathrms    garagepl
    1    0    0    0    1    1    17    1    1    57000.0    0    1    0    0    0    -0.5526794237661955    0.7474571831400939    -0.30482844637640993    0.2539261099118857    1.7548633720450884    -0.7757094582336221
    0    0    1    1    0    0    21    0    1    60000.0    1    1    0    1    0    -0.5526794237661955    0.7474571831400939    0.3087330481306117    -0.8999912756370632    -0.5698449326198075    1.5644560502190699
    0    1    1    1    1    0    22    0    0    27000.0    1    1    0    0    0    -0.5526794237661955    -0.7516329215933905    -0.7064752400883142    -0.8999912756370632    -0.5698449326198075    -0.7757094582336221
    0    0    1    1    1    0    23    0    1    70100.0    1    1    0    0    0    -0.5526794237661955    0.7474571831400939    -0.44641956049341497    0.2539261099118857    -0.5698449326198075    0.39437329599272386
    1    0    0    1    1    0    26    0    1    52000.0    1    1    0    0    0    -0.5526794237661955    0.7474571831400939    -0.7437609001391254    0.2539261099118857    -0.5698449326198075    -0.7757094582336221
    0    1    1    1    1    0    28    1    0    52000.0    0    1    0    0    0    -0.5526794237661955    0.7474571831400939    -1.012784016961435    0.2539261099118857    -0.5698449326198075    -0.7757094582336221
    1    0    0    1    1    0    37    0    1    104900.0    1    1    0    0    0    1.8093671611393762    -2.2507230263268747    2.970645993530306    0.2539261099118857    -0.5698449326198075    0.39437329599272386
    1    0    0    1    1    0    42    1    1    140000.0    0    1    0    0    0    1.8093671611393762    -2.2507230263268747    3.8013138630167354    0.2539261099118857    -0.5698449326198075    1.5644560502190699
    0    0    1    1    1    0    44    0    1    65500.0    1    1    0    0    0    1.8093671611393762    0.7474571831400939    -0.6163288974338209    0.2539261099118857    -0.5698449326198075    0.39437329599272386
    1    0    0    1    1    0    46    0    1    86000.0    1    0    1    0    0    1.8093671611393762    0.7474571831400939    0.8279004665596301    -0.8999912756370632    1.7548633720450884    -0.7757094582336221
                                                                                              
    Testing dataset after scaling:
    fullbase_1    bedrooms_2    fullbase_0    driveway_1    gashw_0    driveway_0    id    airco_1    bedrooms_0    price    airco_0    recroom_0    recroom_1    gashw_1    bedrooms_1    prefarea    homestyle    lotsize    stories    bathrms    garagepl
    1    0    0    1    1    0    13    1    1    99000.0    0    1    0    0    0    -0.5526794237661955    0.7474571831400939    1.7624018197318632    0.2539261099118857    1.7548633720450884    0.39437329599272386
    0    0    1    0    1    1    34    0    1    25245.0    1    1    0    0    0    -0.5526794237661955    -0.7516329215933905    -1.295966245195445    -0.8999912756370632    -0.5698449326198075    -0.7757094582336221
    0    0    1    1    1    0    36    0    1    75000.0    1    0    1    0    0    -0.5526794237661955    0.7474571831400939    2.1966145696906785    0.2539261099118857    1.7548633720450884    1.5644560502190699
    0    0    1    1    1    0    38    0    1    56000.0    1    1    0    0    0    -0.5526794237661955    0.7474571831400939    -1.012784016961435    0.2539261099118857    -0.5698449326198075    -0.7757094582336221
    0    1    1    1    1    0    67    0    0    58500.0    1    1    0    0    0    -0.5526794237661955    0.7474571831400939    -0.5219348213558176    0.2539261099118857    -0.5698449326198075    0.39437329599272386
    0    0    1    1    1    0    72    0    1    70000.0    1    1    0    0    0    -0.5526794237661955    0.7474571831400939    -1.0269431283731354    1.4078434954608345    -0.5698449326198075    -0.7757094582336221
    0    0    1    1    1    0    19    0    1    83900.0    1    1    0    0    0    1.8093671611393762    0.7474571831400939    2.980085401138106    1.4078434954608345    -0.5698449326198075    1.5644560502190699
    1    0    0    1    1    0    27    1    1    120000.0    0    1    0    0    0    1.8093671611393762    -2.2507230263268747    0.16714193401360672    0.2539261099118857    1.7548633720450884    0.39437329599272386
    0    0    1    1    1    0    40    1    1    118500.0    0    1    0    0    0    1.8093671611393762    -2.2507230263268747    -0.12547970182820362    0.2539261099118857    1.7548633720450884    0.39437329599272386
    0    0    1    1    1    0    75    0    1    49500.0    1    1    0    0    0    1.8093671611393762    -0.7516329215933905    -1.4163186921948991    1.4078434954608345    -0.5698449326198075    -0.7757094582336221
                                                                                              
    Total time taken by feature scaling: 51.71 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: 15.19 sec
                                                                                              
    
    1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation
                                                                                              
    Model Training started ...
                                                                                              
    Starting customized hyperparameter update ...
                                                                                              
    Completed customized hyperparameter update.
                                                                                              
    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)
    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)
    Total number of models for decision_forest : 320
    --------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------------
    
                                                                                              
    Performing hyperParameter tuning ...
                                                                                              
    xgboost
    XGBOOST_0                                                                                                                                                                                               
    XGBOOST_1                                                                                 
    XGBOOST_2                                                                                 
                                                                                              
    ----------------------------------------------------------------------------------------------------
                                                                                              
    decision_forest
    DECISIONFOREST_0                                                                                                                                                                                        
    DECISIONFOREST_1                                                                          
    DECISIONFOREST_2                                                                          
                                                                                              
    ----------------------------------------------------------------------------------------------------
                                                                                              
    Evaluating models performance ...
                                                                                              
    Evaluation completed.
                                                                                              
    Leaderboard
    Rank    Name    Feature selection    MAE    MSE    MSLE    RMSE    RMSLE    R2-score    Adjusted R2-score
    0    1    decision_forest    lasso    8623.129721    1.263697e+08    0.027973    11241.429881    0.167251    0.810693    0.782118
    1    2    xgboost    lasso    8829.311362    1.399635e+08    0.027438    11830.619046    0.165644    0.790328    0.758680
    2    3    xgboost    rfe    9938.216125    1.665984e+08    0.032597    12907.299970    0.180545    0.750428    0.710021
    3    4    decision_forest    rfe    9328.937619    1.687526e+08    0.031994    12990.481650    0.178870    0.747201    0.706272
    4    5    xgboost    pca    9751.911610    2.078036e+08    0.036682    14415.395470    0.191525    0.688701    0.660906
    5    6    decision_forest    pca    11300.990329    2.557051e+08    0.048913    15990.782316    0.221163    0.616942    0.582741
                                                                                              
    
    1. Feature Exploration -> 2. Feature Engineering -> 3. Data Preparation -> 4. Model Training & Evaluation
    Completed: |⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿⫿| 100% - 20/20
  5. Display model leaderboard.
    >>> aml.leaderboard()
    
    Rank     Name     Feature selection     MAE     MSE     MSLE     RMSE     RMSLE     R2-score     Adjusted R2-score
    0     1     decision_forest     lasso     8623.129721     1.263697e+08     0.027973     11241.429881     0.167251     0.810693     0.782118
    1     2     xgboost     lasso     8829.311362     1.399635e+08     0.027438     11830.619046     0.165644     0.790328     0.758680
    2     3     xgboost     rfe     9938.216125     1.665984e+08     0.032597     12907.299970     0.180545     0.750428     0.710021
    3     4     decision_forest     rfe     9328.937619     1.687526e+08     0.031994     12990.481650     0.178870     0.747201     0.706272
    4     5     xgboost     pca     9751.911610     2.078036e+08     0.036682     14415.395470     0.191525     0.688701     0.660906
    5     6     decision_forest     pca     11300.990329     2.557051e+08     0.048913     15990.782316     0.221163     0.616942     0.582741
  6. Display the best performing model.
    >>> aml.leader()
    
    Rank     Name     Feature selection     MAE     MSE     MSLE     RMSE     RMSLE     R2-score     Adjusted R2-score
    0     1     decision_forest     lasso     8623.129721     1.263697e+08     0.027973     11241.429881     0.167251     0.810693     0.782118
  7. Generate prediction on validation dataset using best performing model.
    In the data preparation phase, AutoML generates the validation dataset by splitting the data provided during fitting into training and testing sets. AutoML's model training utilizes the training data, with the testing data acting as the validation dataset for model evaluation.
    >>> prediction = aml.predict()
    decision_forest lasso
    
     Prediction : 
        id     prediction  confidence_lower  confidence_upper     price
    0   36   83649.000000      57481.040000     109816.960000   75000.0
    1   38   64793.181818      57397.151515      72189.212121   56000.0
    2  494   81300.000000      54448.000000     108152.000000   71000.0
    3   47   73341.307692      67376.424615      79306.190769   74500.0
    4   72   64793.181818      57397.151515      72189.212121   70000.0
    5   24   81300.000000      54448.000000     108152.000000   99000.0
    6  192   69734.848485      52653.151515      86816.545455   62000.0
    7  566  116750.000000      96660.000000     136840.000000  125000.0
    8  425  119750.000000      93780.000000     145720.000000  120000.0
    9   13   78439.909091      62481.767273      94398.050909   99000.0
    
     Performance Metrics : 
               MAE           MSE      MSLE       MAPE       MPE          RMSE     RMSLE       ME        R2        EV          MPD      MGD
    0  8623.129721  1.263697e+08  0.027973  13.195462  0.292731  11241.429881  0.167251  55250.0  0.810693  0.817014  1732.308762  0.02774
    >>> prediction
    id     prediction     confidence_lower     confidence_upper     price
    13     78439.90909090909     62481.76727272733     94398.05090909085     99000.0
    24     81300.0               54448.0                        108152.0     99000.0
    27     112250.0              90200.0                       134300.0     120000.0
    34     42676.68067226891     32074.974789915963     53278.38655462185     25245.0
    38     64793.18181818182     57397.151515151614     72189.21212121204     56000.0
    40     125250.0              121820.0                       128680.0     118500.0
    36     83649.0               57481.04000000001     109816.95999999999     75000.0
    19     74102.88461538462     61357.23076923094     86848.53846153831     83900.0
    10     61330.70175438597     47148.21052631577     75513.19298245617     54500.0
    9     37133.82352941176      36871.52941176233      37396.1176470612     50000.0
  8. Generate prediction on validation dataset using third best performing model.
    >>> prediction = aml.predict(rank=3)
    xgboost rfe
    
     Prediction : 
        id     Prediction  Confidence_Lower  Confidence_upper     price
    0   36   68813.857253     -10804.513858     148432.228364   75000.0
    1   38   60321.750336      -6258.661347     126902.162020   56000.0
    2  494   85322.187142     -11191.116801     181835.491085   71000.0
    3   47   57942.348652     -14522.348470     130407.045775   74500.0
    4   72   60769.254880      -7751.654224     129290.163984   70000.0
    5   24   95115.039721      -4824.722503     195054.801946   99000.0
    6  192   76269.650005     -12819.436390     165358.736400   62000.0
    7  566  100633.259317     -28860.204458     230126.723093  125000.0
    8  425  125319.849005     -69166.118553     319805.816564  120000.0
    9   13   84398.489489     -13426.216421     182223.195399   99000.0
    
     Performance Metrics : 
               MAE           MSE      MSLE       MAPE       MPE         RMSE     RMSLE            ME        R2        EV          MPD       MGD
    0  9938.216125  1.665984e+08  0.032597  14.147574  4.739233  12907.29997  0.180545  42541.793056  0.750428  0.800898  2218.111088  0.03352
    >>> prediction
    id     Prediction     Confidence_Lower     Confidence_upper     price
    13     84398.48948899999     -13426.21642066157     182223.19539866154     99000.0
    24     95115.0397215         -4824.722503218742     195054.80194621874     99000.0
    27     96762.481205         -31223.794605616946     224748.75701561695     120000.0
    34     38614.38801149999    -3902.8093813540618     81131.58540435405      25245.0
    38     60321.75033649998     -6258.661346652858     126902.16201965281     56000.0
    40     103042.94868899997   -36750.180102314465     242836.07748031442     118500.0
    36     68813.857253         -10804.513857693804     148432.2283636938      75000.0
    19     88164.43012799999     -8743.101611840597     185071.96186784058     83900.0
    10     61134.19488499999    -13832.911327482201     136101.30109748218     54500.0
    9      42904.444797000004     -5409.48853574715     91218.37812974716      50000.0
  9. 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 ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710267622032769"'
    
    Updated dataset after performing customized variable width bin-code transformation :
    homestyle     gashw     lotsize     stories     recroom     sn     price     bathrms     driveway     prefarea     id     fullbase     airco     garagepl     bedrooms
    Classic     no     4040.0     1     no     294     47000.0     1     yes     no     75     no     no     0     small_house
    Classic     yes     4350.0     2     no     198     40500.0     1     no     no     20     no     no     1     big_house
    Classic     no     3970.0     1     no     234     32500.0     1     no     no     36     no     no     0     small_house
    Classic     no     3000.0     1     no     239     26000.0     1     yes     no     44     yes     no     2     small_house
    Classic     no     4960.0     1     no     25     42000.0     1     yes     no     48     no     no     0     small_house
    Classic     no     2610.0     2     no     463     49000.0     1     yes     yes     13     yes     no     0     big_house
    Eclectic     no     3162.0     2     no     161     63900.0     1     yes     no     49     no     yes     1     big_house
    Eclectic     no     9166.0     1     no     53     68000.0     1     yes     no     11     yes     yes     2     small_house
    Eclectic     no     6862.0     2     no     440     69000.0     1     yes     yes     19     no     yes     2     big_house
    Eclectic     no     2787.0     1     no     472     60500.0     1     yes     yes     27     yes     no     0     big_house
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710270465371266"'
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710270412556120"'
    
    Updated dataset after performing customized categorical encoding :
    prefarea     homestyle     gashw     lotsize     stories     recroom     sn     price     bathrms     driveway     id     fullbase     bedrooms     airco     garagepl
    83851.72413793103     2     no     3520.0     1     no     441     51900.0     1     yes     39     no     big_house     no     2
    83851.72413793103     2     no     2176.0     2     yes     469     55000.0     1     yes     8     no     small_house     no     0
    83851.72413793103     2     no     9000.0     1     no     411     90000.0     1     yes     33     yes     big_house     no     1
    83851.72413793103     2     no     7410.0     1     yes     401     92500.0     1     yes     40     yes     big_house     yes     2
    83851.72413793103     2     no     2787.0     1     no     472     60500.0     1     yes     27     yes     big_house     no     0
    83851.72413793103     1     no     2398.0     1     no     459     44555.0     1     yes     21     no     big_house     no     0
    62906.33597883598     1     yes     4350.0     2     no     198     40500.0     1     no     20     no     big_house     no     1
    62906.33597883598     1     no     3970.0     1     no     234     32500.0     1     no     36     no     small_house     no     0
    62906.33597883598     1     no     3000.0     1     no     239     26000.0     1     yes     44     yes     small_house     no     2
    62906.33597883598     1     no     5076.0     1     no     111     43000.0     1     no     52     no     big_house     no     0
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710268137297934"'
    
    Updated dataset after performing categorical encoding :
    prefarea     homestyle     gashw_0     gashw_1     lotsize     stories     recroom_0     recroom_1     sn     price     bathrms     driveway_0     driveway_1     id     fullbase_0     fullbase_1     bedrooms_0     bedrooms_1     bedrooms_2     airco_0     airco_1     garagepl
    62906.33597883598     1     1     0     3630.0     2     1     0     237     43000.0     1     0     1     53     1     0     1     0     0     1     0     3
    62906.33597883598     1     1     0     1700.0     2     1     0     13     27000.0     1     0     1     17     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     3162.0     2     1     0     161     63900.0     1     0     1     49     1     0     1     0     0     0     1     1
    62906.33597883598     2     1     0     9166.0     1     1     0     53     68000.0     1     0     1     11     0     1     0     0     1     0     1     2
    62906.33597883598     2     1     0     2953.0     2     1     0     157     60000.0     1     0     1     31     0     1     1     0     0     0     1     0
    62906.33597883598     2     1     0     5170.0     4     1     0     38     67000.0     1     0     1     12     1     0     1     0     0     0     1     0
    83851.72413793103     2     1     0     3520.0     1     1     0     441     51900.0     1     0     1     39     1     0     1     0     0     1     0     2
    83851.72413793103     2     1     0     2176.0     2     0     1     469     55000.0     1     0     1     8     1     0     0     0     1     1     0     0
    83851.72413793103     2     1     0     9000.0     1     1     0     411     90000.0     1     0     1     33     0     1     1     0     0     1     0     1
    83851.72413793103     2     1     0     7410.0     1     0     1     401     92500.0     1     0     1     40     0     1     1     0     0     0     1     2
    
    Updated dataset after performing customized anti-selection :
    prefarea     homestyle     gashw_0     gashw_1     lotsize     stories     recroom_0     recroom_1     price     bathrms     driveway_0     driveway_1     id     fullbase_0     fullbase_1     bedrooms_0     bedrooms_1     bedrooms_2     airco_0     airco_1     garagepl
    62906.33597883598     1     1     0     3630.0     2     1     0     43000.0     1     0     1     53     1     0     1     0     0     1     0     3
    62906.33597883598     1     1     0     1700.0     2     1     0     27000.0     1     0     1     17     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     3162.0     2     1     0     63900.0     1     0     1     49     1     0     1     0     0     0     1     1
    62906.33597883598     2     1     0     9166.0     1     1     0     68000.0     1     0     1     11     0     1     0     0     1     0     1     2
    62906.33597883598     2     1     0     2953.0     2     1     0     60000.0     1     0     1     31     0     1     1     0     0     0     1     0
    62906.33597883598     2     1     0     5170.0     4     1     0     67000.0     1     0     1     12     1     0     1     0     0     0     1     0
    83851.72413793103     2     1     0     3520.0     1     1     0     51900.0     1     0     1     39     1     0     1     0     0     1     0     2
    83851.72413793103     2     1     0     2176.0     2     0     1     55000.0     1     0     1     8     1     0     0     0     1     1     0     0
    83851.72413793103     2     1     0     9000.0     1     1     0     90000.0     1     0     1     33     0     1     1     0     0     1     0     1
    83851.72413793103     2     1     0     7410.0     1     0     1     92500.0     1     0     1     40     0     1     1     0     0     0     1     2
    Performing transformation carried out in data preparation phase ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710269220059762"'
    
    Updated dataset after performing Lasso feature selection:
    id     gashw_0     airco_1     bedrooms_0     bathrms     airco_0     gashw_1     homestyle     fullbase_1     bedrooms_2     driveway_1     stories     recroom_0     recroom_1     garagepl     prefarea     lotsize     price
    21     1     0     1     1     1     0     1     0     0     1     1     1     0     0     83851.7241     2398.0     44555.0
    32     1     1     1     1     0     0     2     1     0     1     1     0     1     0     83851.7241     6825.0     77500.0
    51     1     0     1     1     1     0     2     0     0     1     1     1     0     0     83851.7241     3520.0     65000.0
    19     1     1     1     1     0     0     2     0     0     1     2     1     0     2     83851.7241     6862.0     69000.0
    10     1     0     0     1     1     0     1     0     1     1     1     1     0     0     62906.336     6000.0     41000.0
    53     1     0     1     1     1     0     1     0     0     1     2     1     0     3     62906.336     3630.0     43000.0
    20     0     0     1     1     1     1     1     0     0     0     2     1     0     1     62906.336     4350.0     40500.0
    57     0     0     1     2     1     1     2     0     0     1     2     1     0     2     62906.336     3630.0     57500.0
    37     0     0     1     1     1     1     2     0     0     1     2     1     0     2     62906.336     3760.0     93000.0
    44     1     0     0     1     1     0     1     1     1     1     1     1     0     2     62906.336     3000.0     26000.0
    
    Updated dataset after performing scaling on Lasso selected features :
    fullbase_1     bedrooms_2     driveway_1     gashw_0     id     airco_1     bedrooms_0     price     airco_0     recroom_0     recroom_1     gashw_1     bathrms     homestyle     stories     garagepl     prefarea     lotsize
    0     0     1     0     37     0     1     93000.0     1     1     0     1     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.654086527865022
    1     0     1     1     27     0     1     60500.0     1     1     0     0     -0.5698449326198072     0.7474571831400922     -0.899991275637062     -0.7757094582336234     1.8093671611394033     -1.1133137079845081
    0     0     1     1     21     0     1     44555.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     1.8093671611394033     -1.2969101859562244
    1     0     1     1     13     0     1     49000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     -0.7757094582336234     1.8093671611394033     -1.196852465313541
    0     0     1     1     51     0     1     65000.0     1     1     0     0     -0.5698449326198072     0.7474571831400922     -0.899991275637062     -0.7757094582336234     1.8093671611394033     -0.7673594191586259
    0     0     1     1     19     1     1     69000.0     0     1     0     0     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     1.8093671611394033     0.8099655921048091
    1     1     1     1     44     0     0     26000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     1.5644560502190725     -0.5526794237661611     -1.0127840169614346
    0     1     1     1     10     0     0     41000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     0.4031271242086149
    0     0     1     1     53     0     1     43000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     2.7345388044454206     -0.5526794237661611     -0.7154426773157242
    1     0     1     1     32     1     1     77500.0     0     0     1     0     -0.5698449326198072     0.7474571831400922     -0.899991275637062     -0.7757094582336234     1.8093671611394033     0.7925026880303785
    
    Updated dataset after performing RFE feature selection:
    id     gashw_0     airco_1     bedrooms_0     bathrms     airco_0     gashw_1     homestyle     fullbase_1     bedrooms_2     fullbase_0     driveway_0     stories     recroom_0     recroom_1     garagepl     prefarea     lotsize     price
    37     0     0     1     1     1     1     2     0     0     1     0     2     1     0     2     62906.336     3760.0     93000.0
    27     1     0     1     1     1     0     2     1     0     0     0     1     1     0     0     83851.7241     2787.0     60500.0
    21     1     0     1     1     1     0     1     0     0     1     0     1     1     0     0     83851.7241     2398.0     44555.0
    13     1     0     1     1     1     0     1     1     0     0     0     2     1     0     0     83851.7241     2610.0     49000.0
    51     1     0     1     1     1     0     2     0     0     1     0     1     1     0     0     83851.7241     3520.0     65000.0
    19     1     1     1     1     0     0     2     0     0     1     0     2     1     0     2     83851.7241     6862.0     69000.0
    44     1     0     0     1     1     0     1     1     1     0     0     1     1     0     2     62906.336     3000.0     26000.0
    10     1     0     0     1     1     0     1     0     1     1     0     1     1     0     0     62906.336     6000.0     41000.0
    53     1     0     1     1     1     0     1     0     0     1     0     2     1     0     3     62906.336     3630.0     43000.0
    32     1     1     1     1     0     0     2     1     0     0     0     1     0     1     0     83851.7241     6825.0     77500.0
    
    Updated dataset after performing scaling on RFE selected features :
    r_bedrooms_2     r_airco_1     r_bedrooms_0     id     price     r_recroom_0     r_gashw_1     r_airco_0     r_driveway_0     r_recroom_1     r_gashw_0     r_fullbase_1     r_fullbase_0     r_bathrms     r_homestyle     r_stories     r_garagepl     r_prefarea     r_lotsize
    0     0     1     21     44555.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     1.8093671611394033     -1.2969101859562244
    0     1     1     32     77500.0     0     0     0     0     1     1     1     0     -0.5698449326198072     0.7474571831400922     -0.899991275637062     -0.7757094582336234     1.8093671611394033     0.7925026880303785
    0     0     1     51     65000.0     1     0     1     0     0     1     0     1     -0.5698449326198072     0.7474571831400922     -0.899991275637062     -0.7757094582336234     1.8093671611394033     -0.7673594191586259
    0     1     1     19     69000.0     1     0     0     0     0     1     0     1     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     1.8093671611394033     0.8099655921048091
    1     0     0     10     41000.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     0.4031271242086149
    0     0     1     53     43000.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     2.7345388044454206     -0.5526794237661611     -0.7154426773157242
    0     0     1     20     40500.0     1     1     1     1     0     0     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     0.3943732959927245     -0.5526794237661611     -0.3756240034349123
    0     0     1     57     57500.0     1     1     1     0     0     0     0     1     1.7548633720450872     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.7154426773157242
    0     0     1     37     93000.0     1     1     1     0     0     0     0     1     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.654086527865022
    1     0     0     44     26000.0     1     0     1     0     0     1     1     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     1.5644560502190725     -0.5526794237661611     -1.0127840169614346
    
    Updated dataset after performing scaling for PCA feature selection :
    fullbase_1     bedrooms_2     fullbase_0     driveway_1     gashw_0     driveway_0     id     airco_1     bedrooms_0     price     airco_0     recroom_0     recroom_1     gashw_1     bedrooms_1     prefarea     homestyle     lotsize     stories     bathrms     garagepl
    0     0     1     1     1     0     21     0     1     44555.0     1     1     0     0     0     1.809367156861831     -0.7516329215933905     -1.296910185956225     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    1     0     0     1     1     0     32     1     1     77500.0     0     0     1     0     0     1.809367156861831     0.7474571831400939     0.7925026880303788     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    0     0     1     1     1     0     51     0     1     65000.0     1     1     0     0     0     1.809367156861831     0.7474571831400939     -0.7673594191586263     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    0     0     1     1     1     0     19     1     1     69000.0     0     1     0     0     0     1.809367156861831     0.7474571831400939     0.8099655921048095     0.2539261099118857     -0.5698449326198075     1.5644560502190699
    0     1     1     1     1     0     10     0     0     41000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     0.4031271242086151     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    0     0     1     1     1     0     53     0     1     43000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -0.7154426773157244     0.2539261099118857     -0.5698449326198075     2.734538804445416
    0     0     1     0     0     1     20     0     1     40500.0     1     1     0     1     0     -0.5526794213794932     -0.7516329215933905     -0.3756240034349125     0.2539261099118857     -0.5698449326198075     0.39437329599272386
    0     0     1     1     0     0     57     0     1     57500.0     1     1     0     1     0     -0.5526794213794932     0.7474571831400939     -0.7154426773157244     0.2539261099118857     1.7548633720450884     1.5644560502190699
    0     0     1     1     0     0     37     0     1     93000.0     1     1     0     1     0     -0.5526794213794932     0.7474571831400939     -0.6540865278650223     0.2539261099118857     -0.5698449326198075     1.5644560502190699
    1     1     0     1     1     0     44     0     0     26000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -1.012784016961435     -0.8999912756370632     -0.5698449326198075     1.5644560502190699
    
    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     33     0.993145     -2.627410     -0.148539     0.704723     -0.232740     0.509951     -0.211075     -0.699905     -0.457163     0.133888     90000.0
    1     20     -0.508801     0.874277     0.823954     0.182310     -0.273324     -0.489318     -0.243253     -0.612992     -0.711796     0.767171     40500.0
    2     27     -0.955663     -1.473711     -1.508791     1.175143     0.453939     -0.816907     -0.056935     -0.221649     -0.365599     -0.380766     60500.0
    3     57     0.764566     0.465882     0.023079     -1.886340     0.908981     -1.265122     -0.991707     -0.333493     0.029300     -0.314790     57500.0
    4     21     -0.908701     -0.347155     -0.555180     2.086322     0.457626     -0.739089     -0.833161     0.086483     -0.453938     -0.814731     44555.0
    5     37     -0.221523     -0.179045     0.708765     -1.118081     -0.668754     -1.510862     -0.382923     -0.493149     -0.036878     -0.246129     93000.0
    6     13     -0.338349     -0.084802     -1.164121     2.082558     0.223640     -0.979941     0.138371     -0.466391     -0.109436     -0.219507     49000.0
    7     32     0.470801     -2.082235     -1.196821     1.131651     -0.030202     0.613375     1.070719     0.477148     -0.176126     -0.287062     77500.0
    8     51     -0.864268     -1.270727     -1.144985     1.118033     -0.045076     -0.506533     -1.007967     0.126552     -0.428159     -0.623289     65000.0
    9     19     1.622531     -1.616476     0.209025     0.323242     -1.060623     -1.062397     -0.470216     0.794823     -0.386081     0.041329     69000.0
    
    Data Transformation completed.
    decision_forest lasso
    
     Prediction : 
       id    prediction  confidence_lower  confidence_upper    price
    0  37  56509.848485      47670.545455      65349.151515  93000.0
    1  27  64793.181818      57397.151515      72189.212121  60500.0
    2  21  42676.680672      32074.974790      53278.386555  44555.0
    3  13  42676.680672      32074.974790      53278.386555  49000.0
    4  51  56509.848485      47670.545455      65349.151515  65000.0
    5  19  74102.884615      61357.230769      86848.538462  69000.0
    6  44  37133.823529      36871.529412      37396.117647  26000.0
    7  10  41085.000000      33078.400000      49091.600000  41000.0
    8  53  42676.680672      32074.974790      53278.386555  43000.0
    9  32  74102.884615      61357.230769      86848.538462  77500.0
    
     Performance Metrics : 
               MAE           MSE      MSLE       MAPE       MPE          RMSE     RMSLE            ME        R2        EV          MPD       MGD
    0  7348.726367  1.025541e+08  0.028796  12.775381 -0.861277  10126.898576  0.169693  36490.151515  0.681496  0.689806  1659.503943  0.029203
    
    
    >>> prediction
    id     prediction     confidence_lower     confidence_upper     price
    10     41085.0               33078.4                         49091.6     41000.0
    12     73341.30769230769     67376.42461538431     79306.19076923106     67000.0
    13     42676.68067226891     32074.974789915963    53278.38655462185     49000.0
    14     42676.68067226891     32074.974789915963    53278.38655462185     48500.0
    16     68949.0               66304.96                       71593.04     72000.0
    17     42676.68067226891     32074.974789915963    53278.38655462185     27000.0
    15     65658.84848484848     56566.11151515146     74751.58545454551     61000.0
    11     68949.0               66304.96                       71593.04     68000.0
    9      62196.368421052626    46317.17052631572     78075.56631578953     55000.0
    8      56114.0350877193      52156.21052631559    60071.859649123006     55000.0
  10. Generate prediction on test dataset using second best performing model.
    >>> prediction = aml.predict(housing_test,2)
    
    Data Transformation started ...
    Performing transformation carried out in feature engineering phase ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710274933135699"'
    
    Updated dataset after performing customized variable width bin-code transformation :
    homestyle     gashw     lotsize     stories     recroom     sn     price     bathrms     driveway     prefarea     id     fullbase     airco     garagepl     bedrooms
    Eclectic     no     7320.0     2     no     540     85000.0     2     yes     no     18     no     no     0     big_house
    Eclectic     no     4080.0     1     no     301     55000.0     1     yes     no     9     no     no     0     small_house
    Eclectic     no     9000.0     1     no     411     90000.0     1     yes     yes     33     yes     no     1     big_house
    Eclectic     no     3162.0     2     no     161     63900.0     1     yes     no     49     no     yes     1     big_house
    Eclectic     no     5885.0     1     no     306     64000.0     1     yes     no     29     no     yes     1     small_house
    Eclectic     yes     3760.0     2     no     117     93000.0     1     yes     no     37     no     no     2     big_house
    Classic     no     3450.0     1     no     251     48500.0     1     yes     no     14     yes     no     2     big_house
    Classic     no     3500.0     1     no     249     44500.0     1     yes     no     25     no     no     0     small_house
    Classic     no     2650.0     2     no     142     40000.0     1     yes     no     41     yes     no     1     big_house
    Classic     yes     4350.0     2     no     198     40500.0     1     no     no     20     no     no     1     big_house
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710268967349630"'
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710269145287484"'
    
    Updated dataset after performing customized categorical encoding :
    prefarea     homestyle     gashw     lotsize     stories     recroom     sn     price     bathrms     driveway     id     fullbase     bedrooms     airco     garagepl
    83851.72413793103     2     no     6420.0     3     no     408     87500.0     1     yes     22     yes     big_house     no     0
    83851.72413793103     1     no     2398.0     1     no     459     44555.0     1     yes     21     no     big_house     no     0
    83851.72413793103     1     no     2610.0     2     no     463     49000.0     1     yes     13     yes     big_house     no     0
    83851.72413793103     2     no     7410.0     1     yes     401     92500.0     1     yes     40     yes     big_house     yes     2
    83851.72413793103     2     no     2787.0     1     no     472     60500.0     1     yes     27     yes     big_house     no     0
    83851.72413793103     2     no     3520.0     1     no     441     51900.0     1     yes     39     no     big_house     no     2
    62906.33597883598     2     yes     3630.0     2     no     176     57500.0     2     yes     57     no     big_house     no     2
    62906.33597883598     2     no     4080.0     1     no     301     55000.0     1     yes     9     no     small_house     no     0
    62906.33597883598     2     no     3162.0     2     no     161     63900.0     1     yes     49     no     big_house     yes     1
    62906.33597883598     2     no     3900.0     2     no     340     62500.0     1     yes     45     no     big_house     no     0
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710269366711677"'
    
    Updated dataset after performing categorical encoding :
    prefarea     homestyle     gashw_0     gashw_1     lotsize     stories     recroom_0     recroom_1     sn     price     bathrms     driveway_0     driveway_1     id     fullbase_0     fullbase_1     bedrooms_0     bedrooms_1     bedrooms_2     airco_0     airco_1     garagepl
    83851.72413793103     1     1     0     2610.0     2     1     0     463     49000.0     1     0     1     13     0     1     1     0     0     1     0     0
    83851.72413793103     2     1     0     2787.0     1     1     0     472     60500.0     1     0     1     27     0     1     1     0     0     1     0     0
    83851.72413793103     2     1     0     3520.0     1     1     0     441     51900.0     1     0     1     39     1     0     1     0     0     1     0     2
    83851.72413793103     2     1     0     9000.0     1     1     0     411     90000.0     1     0     1     33     0     1     1     0     0     1     0     1
    83851.72413793103     2     1     0     6825.0     1     0     1     403     77500.0     1     0     1     32     0     1     1     0     0     0     1     0
    83851.72413793103     2     1     0     3520.0     1     1     0     443     65000.0     1     0     1     51     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     3162.0     2     1     0     161     63900.0     1     0     1     49     1     0     1     0     0     0     1     1
    62906.33597883598     2     1     0     5170.0     4     1     0     38     67000.0     1     0     1     12     1     0     1     0     0     0     1     0
    62906.33597883598     2     1     0     5400.0     2     1     0     177     70000.0     1     0     1     28     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     4360.0     2     1     0     255     61000.0     1     0     1     15     1     0     1     0     0     1     0     0
    
    Updated dataset after performing customized anti-selection :
    prefarea     homestyle     gashw_0     gashw_1     lotsize     stories     recroom_0     recroom_1     price     bathrms     driveway_0     driveway_1     id     fullbase_0     fullbase_1     bedrooms_0     bedrooms_1     bedrooms_2     airco_0     airco_1     garagepl
    62906.33597883598     2     1     0     5400.0     2     1     0     70000.0     1     0     1     28     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     2817.0     2     0     1     78500.0     2     1     0     35     0     1     1     0     0     1     0     1
    62906.33597883598     2     1     0     5000.0     4     1     0     80000.0     1     0     1     67     1     0     1     0     0     1     0     0
    62906.33597883598     2     1     0     4600.0     2     1     0     60000.0     2     0     1     61     1     0     1     0     0     0     1     1
    62906.33597883598     1     1     0     3500.0     1     1     0     44500.0     1     0     1     25     1     0     0     0     1     1     0     0
    62906.33597883598     1     1     0     2650.0     2     1     0     40000.0     1     0     1     41     0     1     1     0     0     1     0     1
    83851.72413793103     2     1     0     6420.0     3     1     0     87500.0     1     0     1     22     0     1     1     0     0     1     0     0
    83851.72413793103     1     1     0     2398.0     1     1     0     44555.0     1     0     1     21     1     0     1     0     0     1     0     0
    83851.72413793103     1     1     0     2610.0     2     1     0     49000.0     1     0     1     13     0     1     1     0     0     1     0     0
    83851.72413793103     2     1     0     7410.0     1     0     1     92500.0     1     0     1     40     0     1     1     0     0     0     1     2
    Performing transformation carried out in data preparation phase ...
    result data stored in table '"AUTOML_USER"."ml__td_sqlmr_persist_out__1710268761943698"'
    
    Updated dataset after performing Lasso feature selection:
    id     gashw_0     airco_1     bedrooms_0     bathrms     airco_0     gashw_1     homestyle     fullbase_1     bedrooms_2     driveway_1     stories     recroom_0     recroom_1     garagepl     prefarea     lotsize     price
    67     1     0     1     1     1     0     2     0     0     1     4     1     0     0     62906.336     5000.0     80000.0
    25     1     0     0     1     1     0     1     0     1     1     1     1     0     0     62906.336     3500.0     44500.0
    41     1     0     1     1     1     0     1     1     0     1     2     1     0     1     62906.336     2650.0     40000.0
    44     1     0     0     1     1     0     1     1     1     1     1     1     0     2     62906.336     3000.0     26000.0
    23     1     1     0     1     0     0     1     0     1     1     1     1     0     0     62906.336     3185.0     37900.0
    47     1     0     1     1     1     0     1     0     0     1     2     1     0     0     62906.336     3850.0     44500.0
    57     0     0     1     2     1     1     2     0     0     1     2     1     0     2     62906.336     3630.0     57500.0
    20     0     0     1     1     1     1     1     0     0     0     2     1     0     1     62906.336     4350.0     40500.0
    37     0     0     1     1     1     1     2     0     0     1     2     1     0     2     62906.336     3760.0     93000.0
    53     1     0     1     1     1     0     1     0     0     1     2     1     0     3     62906.336     3630.0     43000.0
    
    Updated dataset after performing scaling on Lasso selected features :
    fullbase_1     bedrooms_2     driveway_1     gashw_0     id     airco_1     bedrooms_0     price     airco_0     recroom_0     recroom_1     gashw_1     bathrms     homestyle     stories     garagepl     prefarea     lotsize
    0     0     1     1     67     0     1     80000.0     1     1     0     0     -0.5698449326198072     0.7474571831400922     2.5617608810097807     -0.7757094582336234     -0.5526794237661611     -0.0688432561814016
    0     1     1     1     25     0     0     44500.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     -0.7767988267664263
    1     0     1     1     41     0     1     40000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     0.3943732959927245     -0.5526794237661611     -1.1779736500979403
    1     1     1     1     44     0     0     26000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     1.5644560502190725     -0.5526794237661611     -1.0127840169614346
    0     1     1     1     23     1     0     37900.0     0     1     0     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     -0.9254694965892815
    0     0     1     1     47     0     1     44500.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     -0.7757094582336234     -0.5526794237661611     -0.6116091936299205
    0     0     1     0     57     0     1     57500.0     1     1     0     1     1.7548633720450872     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.7154426773157242
    0     0     0     0     20     0     1     40500.0     1     1     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     0.3943732959927245     -0.5526794237661611     -0.3756240034349123
    0     0     1     0     37     0     1     93000.0     1     1     0     1     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.654086527865022
    0     0     1     1     53     0     1     43000.0     1     1     0     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     2.7345388044454206     -0.5526794237661611     -0.7154426773157242
    
    Updated dataset after performing RFE feature selection:
    id     gashw_0     airco_1     bedrooms_0     bathrms     airco_0     gashw_1     homestyle     fullbase_1     bedrooms_2     fullbase_0     driveway_0     stories     recroom_0     recroom_1     garagepl     prefarea     lotsize     price
    67     1     0     1     1     1     0     2     0     0     1     0     4     1     0     0     62906.336     5000.0     80000.0
    25     1     0     0     1     1     0     1     0     1     1     0     1     1     0     0     62906.336     3500.0     44500.0
    41     1     0     1     1     1     0     1     1     0     0     0     2     1     0     1     62906.336     2650.0     40000.0
    44     1     0     0     1     1     0     1     1     1     0     0     1     1     0     2     62906.336     3000.0     26000.0
    23     1     1     0     1     0     0     1     0     1     1     0     1     1     0     0     62906.336     3185.0     37900.0
    47     1     0     1     1     1     0     1     0     0     1     0     2     1     0     0     62906.336     3850.0     44500.0
    57     0     0     1     2     1     1     2     0     0     1     0     2     1     0     2     62906.336     3630.0     57500.0
    20     0     0     1     1     1     1     1     0     0     1     1     2     1     0     1     62906.336     4350.0     40500.0
    37     0     0     1     1     1     1     2     0     0     1     0     2     1     0     2     62906.336     3760.0     93000.0
    53     1     0     1     1     1     0     1     0     0     1     0     2     1     0     3     62906.336     3630.0     43000.0
    
    Updated dataset after performing scaling on RFE selected features :
    r_bedrooms_2     r_airco_1     r_bedrooms_0     id     price     r_recroom_0     r_gashw_1     r_airco_0     r_driveway_0     r_recroom_1     r_gashw_0     r_fullbase_1     r_fullbase_0     r_bathrms     r_homestyle     r_stories     r_garagepl     r_prefarea     r_lotsize
    0     0     1     67     80000.0     1     0     1     0     0     1     0     1     -0.5698449326198072     0.7474571831400922     2.5617608810097807     -0.7757094582336234     -0.5526794237661611     -0.0688432561814016
    1     0     0     25     44500.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     -0.7767988267664263
    0     0     1     41     40000.0     1     0     1     0     0     1     1     0     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     0.3943732959927245     -0.5526794237661611     -1.1779736500979403
    1     0     0     44     26000.0     1     0     1     0     0     1     1     0     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     1.5644560502190725     -0.5526794237661611     -1.0127840169614346
    1     1     0     23     37900.0     1     0     0     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     -0.899991275637062     -0.7757094582336234     -0.5526794237661611     -0.9254694965892815
    0     0     1     47     44500.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     -0.7757094582336234     -0.5526794237661611     -0.6116091936299205
    0     0     1     57     57500.0     1     1     1     0     0     0     0     1     1.7548633720450872     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.7154426773157242
    0     0     1     20     40500.0     1     1     1     1     0     0     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     0.3943732959927245     -0.5526794237661611     -0.3756240034349123
    0     0     1     37     93000.0     1     1     1     0     0     0     0     1     -0.5698449326198072     0.7474571831400922     0.25392610991188536     1.5644560502190725     -0.5526794237661611     -0.654086527865022
    0     0     1     53     43000.0     1     0     1     0     0     1     0     1     -0.5698449326198072     -0.7516329215933888     0.25392610991188536     2.7345388044454206     -0.5526794237661611     -0.7154426773157242
    
    Updated dataset after performing scaling for PCA feature selection :
    fullbase_1     bedrooms_2     fullbase_0     driveway_1     gashw_0     driveway_0     id     airco_1     bedrooms_0     price     airco_0     recroom_0     recroom_1     gashw_1     bedrooms_1     prefarea     homestyle     lotsize     stories     bathrms     garagepl
    0     0     1     1     1     0     67     0     1     80000.0     1     1     0     0     0     -0.5526794213794932     0.7474571831400939     -0.06884325618140162     2.5617608810097834     -0.5698449326198075     -0.7757094582336221
    0     1     1     1     1     0     25     0     0     44500.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -0.7767988267664266     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    1     0     0     1     1     0     41     0     1     40000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -1.1779736500979407     0.2539261099118857     -0.5698449326198075     0.39437329599272386
    1     1     0     1     1     0     44     0     0     26000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -1.012784016961435     -0.8999912756370632     -0.5698449326198075     1.5644560502190699
    0     1     1     1     1     0     23     1     0     37900.0     0     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -0.9254694965892819     -0.8999912756370632     -0.5698449326198075     -0.7757094582336221
    0     0     1     1     1     0     47     0     1     44500.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -0.6116091936299208     0.2539261099118857     -0.5698449326198075     -0.7757094582336221
    0     0     1     1     0     0     57     0     1     57500.0     1     1     0     1     0     -0.5526794213794932     0.7474571831400939     -0.7154426773157244     0.2539261099118857     1.7548633720450884     1.5644560502190699
    0     0     1     0     0     1     20     0     1     40500.0     1     1     0     1     0     -0.5526794213794932     -0.7516329215933905     -0.3756240034349125     0.2539261099118857     -0.5698449326198075     0.39437329599272386
    0     0     1     1     0     0     37     0     1     93000.0     1     1     0     1     0     -0.5526794213794932     0.7474571831400939     -0.6540865278650223     0.2539261099118857     -0.5698449326198075     1.5644560502190699
    0     0     1     1     1     0     53     0     1     43000.0     1     1     0     0     0     -0.5526794213794932     -0.7516329215933905     -0.7154426773157244     0.2539261099118857     -0.5698449326198075     2.734538804445416
    
    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     28     -0.755826     0.089334     -0.429328     -0.470881     -0.999024     0.473739     -0.329050     -0.348314     -0.178287     -0.320894     70000.0
    1     57     0.764566     0.465882     0.023079     -1.886340     0.908981     -1.265122     -0.991707     -0.333493     0.029300     -0.314790     57500.0
    2     35     0.161208     0.573101     -1.111763     -1.341894     1.626931     -0.779797     0.666541     -0.836778     -0.009257     0.805046     78500.0
    3     20     -0.508801     0.874277     0.823954     0.182310     -0.273324     -0.489318     -0.243253     -0.612992     -0.711796     0.767171     40500.0
    4     67     0.021937     1.350925     -1.173058     -0.539117     -2.189171     -0.137626     -0.234893     -0.614786     0.433529     0.224336     80000.0
    5     37     -0.221523     -0.179045     0.708765     -1.118081     -0.668754     -1.510862     -0.382923     -0.493149     -0.036878     -0.246129     93000.0
    6     61     0.856232     0.622262     -0.527916     -1.576152     0.603875     -0.201855     -0.339469     0.966263     -0.326257     -0.372249     60000.0
    7     25     -1.701373     0.405676     0.680010     0.610730     0.412048     0.370640     -0.388688     0.263257     0.585903     -0.277915     44500.0
    8     41     -0.636762     0.683363     0.370194     0.250197     0.172249     -1.071549     0.807644     -0.771370     -0.084296     -0.252319     40000.0
    9     44     -0.800879     -0.342758     1.687162     0.011926     1.006483     -1.281534     0.537095     -0.280733     0.720541     0.141092     26000.0
    Data Transformation completed.
    xgboost lasso
    
     Prediction : 
       id    Prediction  Confidence_Lower  Confidence_upper    price
    0  37  62714.069520      -6316.730985     131744.870026  93000.0
    1  35  73655.451675     -12428.314050     159739.217399  78500.0
    2  67  76036.607300      -9087.881354     161161.095955  80000.0
    3  61  70711.370597      -6294.423867     147717.165061  60000.0
    4  41  42599.782586      -6304.166187      91503.731359  40000.0
    5  44  41463.151315      -8363.938672      91290.241302  26000.0
    6  53  42421.980720      -2177.117509      87021.078948  43000.0
    7  23  46037.600206      -5849.901030      97925.101443  37900.0
    8  47  41078.694009      -8495.176343      90652.564361  44500.0
    9  25  41655.907478      -6055.580679      89367.395634  44500.0
    
     Performance Metrics : 
              MAE           MSE      MSLE       MAPE       MPE         RMSE     RMSLE           ME        R2       EV          MPD       MGD
    0  7134.81139  9.188730e+07  0.027124  12.711305 -1.555922  9585.786355  0.164694  30285.93048  0.714624  0.71873  1503.427593  0.026917
    
    
    >>> prediction
    id     Prediction     Confidence_Lower     Confidence_upper     price
    10     42046.118813500005    -8584.041706275093     92676.2793332751      41000.0
    12     77229.41129799999     -8541.168422426272     162999.99101842626    67000.0
    13     40138.81184550001     -9919.126044487813     90196.74973548783     49000.0
    14     43532.443208000004    -4219.128768068425     91284.01518406844     48500.0
    16     71090.69293349999     -8096.803697355877     150278.18956435585    72000.0
    17     39847.6361325         -7393.7174402037635    87088.98970520377     27000.0
    15     63124.04753950001     -10357.04184447629     136605.1369234763     61000.0
    11     69016.39639699999     -9225.572594512676     147258.36538851267    68000.0
    9      59340.18782399999     -13665.002609753996    132345.37825775397    55000.0
    8      68133.27699           -16088.531572887572    152355.08555288758    55000.0