SHAP Value Calculation and Visualization for Models

This function uses the SHAP library to calculate SHAP values for a given model and optionally visualizes these values using the force_plot function. SHAP values are a method to interpret model predictions, measuring the impact of each feature on the model's prediction.

Technology Stack : SHAP, Numpy

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def predict_shap_values(model, X, feature_names=None):
    import shap
    import numpy as np
    
    # Create a SHAP explainer for the model
    explainer = shap.TreeExplainer(model)
    
    # Compute SHAP values for the data
    shap_values = explainer.shap_values(X)
    
    # If feature names are provided, use them to label the SHAP values
    if feature_names is not None:
        shap.initjs()
        shap.force_plot(explainer.expected_value[0], shap_values[0], X[0], feature_names=feature_names)
    
    return shap_values