Generating SHAP Feature Importance Plot

This function uses the SHAP library to generate a feature importance plot for a specified feature. It first creates a SHAP explainer, then computes the SHAP values for the feature, and displays these values using a waterfall plot.

Technology Stack : SHAP library, pandas DataFrame, machine learning model, SHAP explainer, SHAP values, waterfall plot

Code Type : The type of code

Code Difficulty : Intermediate

                
                    
def random_shap_feature_importance(df, feature, model):
    """
    Generate a feature importance plot using SHAP values for a given feature and model.
    
    Args:
    df (pandas.DataFrame): The input DataFrame containing the data.
    feature (str): The name of the feature for which to compute importance.
    model: A trained machine learning model.
    """
    import shap
    explainer = shap.Explainer(model, df)
    shap_values = explainer([feature])
    shap.plots.waterfall(shap_values[0])