DrWhy is the collection of tools for eXplainable AI (XAI). It's based on shared principles and simple grammar for exploration, explanation and visualisation of predictive models.

Survival Analysis and Visualization

📍 Interactive Studio for Explanatory Model Analysis

R tools for Eurostat data

A set of tools to understand what is happening inside a Random Forest

R package for object detection and image segmentation.

SHAP Plots in R

modelDown generates a website with HTML summaries for predictive models

Explainable Machine Learning in Survival Analysis

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

Flexible tool for bias detection, visualization, and mitigation

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

Extensions for the DALEX package

Different SHAP algorithms

Model verification, validation, and error analysis

An R wrapper of SHAP python library

Workshop: Explanation and exploration of machine learning models with R and DALEX at eRum 2020

Effects and Importances of Model Ingredients

Data generator for Arena - interactive XAI dashboard

Surrogate Assisted Feature Extraction in R

R package EloML: Elo rating system for machine learning models

Create Shiny application with model exploration from explainers

R package for Multisource Embeddings for Medical Records

datasets from PISA 2012 study

Visualize correlations between variables

R package for automated analysis of serological data

Triplot: Instance- and data-level explanations for the groups of correlated features.

Performance is not enough: the story of Rashomon's quartet

Create web API from model explainers