Quickly build Explainable AI dashboards that show the inner workings of so-called "blackbox" machine learning models.

A demonstration of the explainerdashboard package that that displays model quality, permutation importances, SHAP values and interactions, and individual trees for sklearn RandomForestClassifiers, etc

A minimal, reproducible explainable-AI demo using SHAP values on tabular data. Trains RandomForest or LogisticRegression models, computes global and local feature importances, and visualizes results through summary and dependence plots, all in under 100 lines of Python.

Multiclass Skin lesion localization and Detection with YOLOv7-XAI Framework with explainable AI