Interpretable Machine Learning
A guide for making black box models explainable.
lime shapely shap interpretability explainability black-box tutorial article

After exploring the concepts of interpretability, you will learn about simple, interpretable models such as decision trees, decision rules and linear regression. Later chapters focus on general model-agnostic methods for interpreting black box models like feature importance and accumulated local effects and explaining individual predictions with Shapley values and LIME.

Don't forget to tag @christophm in your comment, otherwise they may not be notified.

Interpretable Machine Learning researcher. Author of Interpretable Machine Learning Book:
Share this project
Similar projects
How to Explain the Prediction of a Machine Learning Model?
Model interpretability, covering two aspects: (i) interpretable models w/ model-specific interpretation methods & (ii) approaches of explaining black-box ...
Lime: Local Interpretable Model-Agnostic Explanations
Explains the predictions of any classifier in an interpretable and faithful manner, by learning an interpretable model locally around the prediction.
Fit interpretable machine learning models. Explain blackbox machine learning.
Identification of contributing features towards the rupture risk prediction of intracranial aneurysms using LIME explainer
Top collections