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/

[CONTRIBUTORS WELCOME] Generalized Additive Models in Python

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 library for Interpretable Machine Learning in Text Classification using the SS3 model, with easy-to-use visualization tools for Explainable AI

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

Explainable Machine Learning in Survival Analysis

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"

Optimal Sparse Decision Trees

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

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

A list of research papers of explainable machine learning.

A PyTorch implementation of constrained optimization and modeling techniques