ModelOriented/shapviz

A general framework for constructing partial dependence (i.e., marginal effect) plots from various types machine learning models in R.

Explaining the output of machine learning models with more accurately estimated Shapley values

Local Interpretable Model-Agnostic Explanations (R port of original Python package)

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

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

Shapley Interactions and Shapley Values for Machine Learning

Graphical analysis of random forests with the randomForestSRC, randomForest, varPro and ggplot2 packages.

For calculating global feature importance using Shapley values.

Lime: Explaining the predictions of any machine learning classifier