26 Open source projects that are alternatives of or similar to ShapML.jl

Techniques & resources for training interpretable ML models, explaining ML models, and debugging ML models.

The code of AAAI 2020 paper "Transparent Classification with Multilayer Logical Perceptrons and Random Binarization".

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

This package can be used for dominance analysis or Shapley Value Regression for finding relative importance of predictors on given dataset. This library can be used for key driver analysis or marginal resource allocation models.

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

Fast approximate Shapley values in R

A curated list of awesome machine learning interpretability resources.

Fit interpretable models. Explain blackbox machine learning.

Sample use case for Xavier AI in Healthcare conference: https://www.xavierhealth.org/ai-summit-day2/

Concept Bottleneck Models, ICML 2020

Using / reproducing ACD from the paper "Hierarchical interpretations for neural network predictions" 🧠 (ICLR 2019)

Data generator for Arena - interactive XAI dashboard

Reference tables to introduce and organize evaluation methods and measures for explainable machine learning systems

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

Routines and data structures for using isarn-sketches idiomatically in Apache Spark

Model interpretability and understanding for PyTorch

In this project I aim to apply Various Predictive Maintenance Techniques to accurately predict the impending failure of an aircraft turbofan engine.

Code for using CDEP from the paper "Interpretations are useful: penalizing explanations to align neural networks with prior knowledge" https://arxiv.org/abs/1909.13584

A lightweight implementation of removal-based explanations for ML models.

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

For calculating global feature importance using Shapley values.

Multi-Touch Attribution

Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers

FairPut - Machine Learning Fairness Framework with LightGBM — Explainability, Robustness, Fairness (by @firmai)

ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021

Implementation of the paper "Shapley Explanation Networks"