shap · PyPI
pypi.org › project › shapOct 20, 2021 · SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.
Shap :: Anaconda.org
https://anaconda.org/conda-forge/shapSHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.
pip install shap fail · Issue #539 · slundberg/shap · GitHub
github.com › slundberg › shapApr 08, 2019 · Complete output from command c:\users\chenl\anaconda3\envs\dand\python.exe -u -c "import setuptools, tokenize;file='c:\users\chenl\appdata\local\temp\pip-install-den8yg\shap\setup.py';f=getattr(tokenize, 'open', open)(file);code=f.read().replace('\r ', ' ');f.close();exec(compile(code, file, 'exec'))" install --record c:\users\chenl\appdata\local\temp\pip-record-gt_ov7\install-record.txt --single-version-externally-managed --compile:
shape · PyPI
pypi.org › project › shapeApr 09, 2016 · shape 1.0.0 pip install shape Copy PIP instructions. Latest version. Released: Apr 9, 2016 UNKNOWN. Navigation. Project description Release history ...
shap 0.40.0 on PyPI - Libraries.io
https://libraries.io/pypi/shapInstall pip install shap==0.40.0 SourceRank 17. Dependencies 0 Dependent packages 153 Dependent repositories 68 Total releases 94 Latest release Oct 20, 2021 First release Dec 1, 2016 Stars 591 Forks 0 Watchers 1 Contributors 1 Repository size 143 KB Documentation. Shap ...
shap · PyPI
https://pypi.org/project/shap20.10.2021 · SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model. SHAP connects game theory with local explanations, uniting several previous methods and representing the only possible consistent and locally accurate additive feature attribution method based on expectations.