xai-feature-selection 0.3

Feature selection using XAI

feature selection

Advanced feature selection using explainable AI

Developed by Yaganteeswarudu Akkem , L Saran Kaanth , Sujith Kumar Reddy

Examples of How To Use Feature selection

Install package by using below syntax

pip install xai-feature-selection==0.3

Consume package by using below syntax

from xai_feature_selection.feature_selection import FeatureSelect

from xai_feature_selection.model_prediction import Model

Currently xai_feature_selection built to work for classification and regression problems

Use below algorithms to test regession

1)LinearRegression

2)RandomForestRegressor

Use below algorithm for classification

1)LogisticRegression

Below is the syntax to retrieve best features after calculating feature importance

file_path: location of csv file in your system

predict_columns : in classification or regression , column which is going to be predicted

model_type_choice : 0 - Regression

                 1 - Classification

model_choice :

           For regression 

           0 - LinearRegression

           1 - RandomForestRegressor

           for classification 

           0 - LogisticRegression

Once all parameters choosen , simply use below syntax to call Model , to calculate LIME and SHAP values and finally Feature select method will return important features

            if predict_columns and file_path:

                model = Model(

                    model_type=model_type_choice,

                    model_choice=model_choice,

                    data_file_path=file_path,

                    predict_columns=predict_columns,

                )

                model.train()

                lime_data, shap_data = model.explain()

                feature_handler = FeatureSelect(

                    shap_data=shap_data, lime_data=lime_data

                )

                feature_handler.prepare_weights()

                feature_handler.calculate_feature_values()

                feature_handler.get_best_feature_data()

                print(feature_handler.get_best_feature_data())

Note :

Its very important if you pass more appropriate pre-processed data ( without null values , outliers and so on ) , you will expect more better features from algorithm