ML Model Interpretability

Quick Start

import shap
model = load_model()
explainer = shap.TreeExplainer(model)
shap_values = explainer.shap_values(X)
shap.summary_plot(shap_values, X)

Purpose

Design interpretability strategies combining global explanations (which features matter overall) and local explanations (why this specific prediction) with appropriate visualizations.

Architecture/Decision Trees