Explainable AI: Making AI Decisions Transparent

Explainable AI (XAI) makes AI decisions more interpretable.

Tools like SHAP and LIME provide insights into model predictions.

Example: Using SHAP for Model Explainability

import shap
import xgboost as xgb

model = xgb.XGBClassifier()
explainer = shap.Explainer(model)
shap_values = explainer.shap_values(X_test)
shap.summary_plot(shap_values, X_test)


This code generates a SHAP summary plot to interpret feature importance.