xai-feature-selection 0.5

Feature selection using XAI

Advanced feature selection using explainable Artificial Intelligence (XAI)

Developed by Yaganteeswarudu Akkem , Data scientist , Ph.D. Scholar , NIT Silchar

Introduction

In the rapidly evolving field of machine learning, the complexity of models is ever-increasing, necessitating sophisticated feature selection techniques to enhance predictive performance and shed light on the decision-making processes. This study presents an innovative architecture that synergizes the global explanation capabilities of SHAP (SHapley Additive exPlanations) with the local interpretability provided by LIME (Local Interpretable Model-agnostic Explanations) to advance the feature selection process.

Our proposed methodology harnesses the strengths of both SHAP and LIME, systematically identifying features that wield consistent influence across the entire dataset as well as those vital to individual predictions. By normalizing SHAP values to derive feature weights and integrating these with LIME scores, we formulate a maximum interpretation score for each feature. This hybrid framework offers a refined and nuanced approach to feature selection, adeptly balancing the pursuit of model simplicity with the demands for high predictive accuracy and interpretability. The architecture not only promises substantial enhancements in computational efficiency and model performance but also holds significant promise for applications where model transparency and decision-making understanding are critical.

Examples of How To Use Feature selection

Install package by using below syntax

pip install xai-feature-selection==0.4

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