📍 Interactive Studio for Explanatory Model Analysis

Model Agnostics breakDown plots

Break Down with interactions for local explanations (SHAP, BreakDown, iBreakDown)

Compute SHAP values for your tree-based models using the TreeSHAP algorithm

Interesting resources related to Explainable Artificial Intelligence, Interpretable Machine Learning, Interactive Machine Learning, Human in Loop and Visual Analytics.

An R package for computing asymmetric Shapley values to assess causality in any trained machine learning model

Local Interpretable (Model-agnostic) Visual Explanations - model visualization for regression problems and tabular data based on LIME method. Available on CRAN

Data generator for Arena - interactive XAI dashboard

Surrogate Assisted Feature Extraction in R

Implementation of the Anchors algorithm: Explain black-box ML models

Variable importance via oscillations

Micro-reserve model using XGBoost

Explain! Package with core wrappers for DrWhy universe.

Application of predictive models on a real data set of the obstetric medicine field and methods of interpretability on the previously fitted XGBoost model.