interpretable-machine-learning
There are 306 repositories under interpretable-machine-learning topic.
interpretml/interpret
Fit interpretable models. Explain blackbox machine learning.
jphall663/awesome-machine-learning-interpretability
A curated list of awesome responsible machine learning resources.
ModelOriented/DALEX
moDel Agnostic Language for Exploration and eXplanation
interpretml/DiCE
Generate Diverse Counterfactual Explanations for any machine learning model.
SelfExplainML/PiML-Toolbox
PiML (Python Interpretable Machine Learning) toolbox for model development & diagnostics
dswah/pyGAM
[HELP REQUESTED] Generalized Additive Models in Python
salesforce/OmniXAI
OmniXAI: A Library for eXplainable AI
pbiecek/xai_resources
Interesting resources related to XAI (Explainable Artificial Intelligence)
jphall663/interpretable_machine_learning_with_python
Examples of techniques for training interpretable ML models, explaining ML models, and debugging ML models for accuracy, discrimination, and security.
h2oai/mli-resources
H2O.ai Machine Learning Interpretability Resources
explainX/explainx
Explainable AI framework for data scientists. Explain & debug any blackbox machine learning model with a single line of code. We are looking for co-authors to take this project forward. Reach out @ ms8909@nyu.edu
sergioburdisso/pyss3
A Python package implementing a new interpretable machine learning model for text classification (with visualization tools for Explainable AI :octocat:)
ModelOriented/modelStudio
📍 Interactive Studio for Explanatory Model Analysis
hbaniecki/adversarial-explainable-ai
💡 Adversarial attacks on explanations and how to defend them
salesforce/ETSformer
PyTorch code for ETSformer: Exponential Smoothing Transformers for Time-series Forecasting
idealo/cnn-exposed
🕵️♂️ Interpreting Convolutional Neural Network (CNN) Results.
jrieke/cnn-interpretability
🏥 Visualizing Convolutional Networks for MRI-based Diagnosis of Alzheimer’s Disease
yewsiang/ConceptBottleneck
Concept Bottleneck Models, ICML 2020
Graph-COM/GSAT
[ICML 2022] Graph Stochastic Attention (GSAT) for interpretable and generalizable graph learning.
pietrobarbiero/pytorch_explain
PyTorch Explain: Interpretable Deep Learning in Python.
bgreenwell/fastshap
Fast approximate Shapley values in R
andreysharapov/xaience
All about explainable AI, algorithmic fairness and more
dylan-slack/TalkToModel
TalkToModel gives anyone with the powers of XAI through natural language conversations 💬!
fzi-forschungszentrum-informatik/TSInterpret
An Open-Source Library for the interpretability of time series classifiers
rachtibat/zennit-crp
An eXplainable AI toolkit with Concept Relevance Propagation and Relevance Maximization
atulshanbhag/Layerwise-Relevance-Propagation
Implementation of Layerwise Relevance Propagation for heatmapping "deep" layers
12wang3/rrl
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"
ml-for-high-risk-apps-book/Machine-Learning-for-High-Risk-Applications-Book
Official code repo for the O'Reilly Book - Machine Learning for High-Risk Applications
ModelOriented/survex
Explainable Machine Learning in Survival Analysis
VincentGranville/Machine-Learning
Material related to my book Intuitive Machine Learning. Some of this material is also featured in my new book Synthetic Data and Generative AI.
M-Nauta/ProtoTree
ProtoTrees: Neural Prototype Trees for Interpretable Fine-grained Image Recognition, published at CVPR2021
charmlab/mace
Model Agnostic Counterfactual Explanations
inouye-lab/ShapleyExplanationNetworks
Implementation of the paper "Shapley Explanation Networks"
nredell/ShapML.jl
A Julia package for interpretable machine learning with stochastic Shapley values
ModelOriented/treeshap
Compute SHAP values for your tree-based models using the TreeSHAP algorithm