github Actively maintained

shap/shap

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

Quick read

Stars
25,663
Forks
3,733
Open issues
1,042
Commits
3,034

Activity and growth

Latest capture 2026-08-03 03:11

Stars · last 7 days
No history
Commits · last 7 days
No history
Stars since tracking
+191
Stored snapshots
8

Classification

Metadata

Language
Jupyter Notebook
License
MIT
Default branch
master
Created
2016-11-22
First commit
2016-11-22
Last pushed
2026-07-28
GitHub updated
2026-08-02
Last synced
2026-08-03 03:11
Stack scanned
2026-08-03 03:11
Archived
No

AI development signals

6 paths

Agent instructions and tool configuration found in this repository.

Growth history

Tracked growth

8 observed captures since 2026-05-22. Observed captures are shown by default.

Stars from first capture +191

Chart data

Observed captures only

Time horizon

All tracked data

Custom date range

Stars history

Observed snapshots

Commits history

Observed snapshots

Similar repositories

Nearest indexed repositories by embedding similarity.

MAIF/shapash

🔅 Shapash: User-friendly Explainability and Interpretability to Develop Reliable and Transparent Machine Learning Models

3,252 stars
Jupyter Notebook 2 awesome lists

benedekrozemberczki/shapley

The official implementation of "The Shapley Value of Classifiers in Ensemble Games" (CIKM 2021).

227 stars
Python 3 awesome lists

meta-pytorch/captum

Model interpretability and understanding for PyTorch

5,681 stars
Python 1 awesome list

interpretml/interpret

Fit interpretable models. Explain blackbox machine learning.

6,907 stars
C++ 3 awesome lists

SeldonIO/alibi

Algorithms for explaining machine learning models

2,643 stars
Python 1 awesome list

SakanaAI/AI-Scientist

The AI Scientist: Towards Fully Automated Open-Ended Scientific Discovery 🧑‍🔬

14,320 stars
Jupyter Notebook 1 awesome list