Analyzing Model Impact with SHAP Values

This function uses the SHAP library to analyze the impact of each feature on the predictions of a given model. It visualizes the SHAP values for a subset of test data points.

Technology Stack : SHAP library, numpy, scikit-learn's RandomForestClassifier, shap

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
import numpy as np
import shap
from sklearn.ensemble import RandomForestClassifier

def analyze_model_impact(X_train, y_train, X_test, model):
    """
    This function uses SHAP values to analyze the impact of each feature on the predictions of a given model.
    It visualizes the SHAP values for a subset of test data points.
    """
    
    # Create a SHAP explainer for the model
    explainer = shap.TreeExplainer(model)
    
    # Compute SHAP values for the test data
    shap_values = explainer.shap_values(X_test)
    
    # Plot the SHAP values for the first few test data points
    shap.initjs()
    shap.force_plot(explainer.expected_value[1], shap_values[1][0], X_test[0])
    shap.force_plot(explainer.expected_value[0], shap_values[0][0], X_test[0])
    
    return shap_values