csinva/imodels

Fit interpretable models. Explain blackbox machine learning.

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

A game theoretic approach to explain the output of any machine learning model.

Lime: Explaining the predictions of any machine learning classifier

Interpretability and explainability of data and machine learning models

Lazy Predict help build a lot of basic models without much code and helps understand which models works better without any parameter tuning

The Learning Interpretability Tool: Interactively analyze ML models to understand their behavior in an extensible and framework agnostic interface.

Fast and Accurate ML in 3 Lines of Code

✨ Build a machine learning model from a prompt