interpretable-ml

Fit interpretable models. Explain blackbox machine learning.

Model interpretability and understanding for PyTorch

A curated list of awesome responsible machine learning resources.

High-Performance Symbolic Regression in Python and Julia

Pytorch-based tools for visualizing and understanding the neurons of a GAN. https://gandissect.csail.mit.edu/

Distributed High-Performance Symbolic Regression in Julia

Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.

H2O.ai Machine Learning Interpretability Resources

A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI <img alt="octocat" src="https://github.githubassets.com/images/icons/emoji/octocat.png?v8" />)

Zennit is a high-level framework in Python using PyTorch for explaining/exploring neural networks using attribution methods like LRP.

An Open-Source Library for the interpretability of time series classifiers

The code of NeurIPS 2021 paper "Scalable Rule-Based Representation Learning for Interpretable Classification" and TPAMI paper "Learning Interpretable Rules for Scalable Data Representation and Classification"

Explainable Machine Learning in Survival Analysis

Optimal Sparse Decision Trees

PIP-Net: Patch-based Intuitive Prototypes Network for Interpretable Image Classification (CVPR 2023)

[NeurIPS 2023] This is the official code for the paper "TPSR: Transformer-based Planning for Symbolic Regression"

A list of research papers of explainable machine learning.

A PyTorch implementation of constrained optimization and modeling techniques

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