Demystifying the Black Box: A Hands-On Guide to Explainable AI (XAI) TL;DR Introduction The Core Engine: XGBoost on Heart Disease Data Pillar 1: Permutation Imp
This guide explores Explainable AI (XAI) techniques to demystify complex machine learning models. It focuses on practical applications using XGBoost for a heart disease classifier, demonstrating how to build trust in AI decisions. The guide covers methods like Permutation Importance, Partial Dependence Plots, and SHAP values to reveal how features influence predictions and provide both local and global explanations. AI
IMPACT Provides practical methods for understanding and trusting AI models, crucial for adoption in sensitive domains like healthcare.