Analyzing Model Interpretability with SHAP Values

This code defines a function that uses the SHAP library to analyze the interpretability of a machine learning model. It accepts a trained model and input data, computes SHAP values, and visualizes them using waterfall plots.

Technology Stack : The code uses the SHAP library to analyze the interpretability of a machine learning model. It accepts a trained model and input data, computes SHAP values, and visualizes them using waterfall plots.

Code Type : The type of code

Code Difficulty : Advanced

                
                    
import numpy as np
import shap

def analyze_model_explanation(model, X):
    """
    Analyze the explanation of a model using SHAP values.
    
    This function uses SHAP (SHapley Additive exPlanations) to explain the predictions of a model.
    It computes SHAP values for the given data and visualizes them using SHAP force plots.
    
    Args:
        model (sklearn.base.BaseEstimator): The trained machine learning model to explain.
        X (np.array or pd.DataFrame): The input data for which explanations are computed.
    
    Returns:
        shap.Explanation: The SHAP explanation object containing the SHAP values.
    """
    explainer = shap.Explainer(model)
    shap_values = explainer(X)
    shap.plots.waterfall(shap_values, X)
    return shap_values