shap/shap

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

Jupyter NotebookPythonC++JavaScriptCudaPowerShellmachine-learningdeep-learninggradient-boostinginterpretabilityshapleyshapexplainability
This is stars and forks stats for /shap/shap repository. As of 28 Apr, 2024 this repository has 20226 stars and 3020 forks.

SHAP (SHapley Additive exPlanations) is a game theoretic approach to explain the output of any machine learning model. It connects optimal credit allocation with local explanations using the classic Shapley values from game theory and their related extensions (see papers for details and citations). Install SHAP can be installed from either PyPI or conda-forge: pip install shap or conda install -c conda-forge shap Tree ensemble example (XGBoost/LightGBM/CatBoost/scikit-learn/pyspark models) While...
Read on GithubGithub Stats Page
repotechsstarsweeklyforksweekly
DigitalHumanSpace/MetaHuman_DNA_ToolsMathematicaPythonC++15020
jinyeying/nighttime_dehazePythonMATLABM109060
senzee1984/Amsi_Bypass_In_2023PowerShell276+1480
Manisso/fsocietyPythonShellDockerfile9.5k01.9k0
bps-statistics/stadataPython1220140
kelvins/awesome-mlopsPython3k04490
chapyter/chapyterPythonTypeScriptCSS6060420
victoricoma/appThoughtsJavaScriptHandlebarsCSS00230
OpenBMB/AgentVerseJavaScriptTypeScriptPython2.5k02190
p1atdev/LECOJupyter NotebookPython1750140