shap - PyPI
pypi.org › project › shapOct 20, 2021 · pip install shap Copy PIP instructions Latest version Released: Oct 20, 2021 A unified approach to explain the output of any machine learning model. Project description SHAP (SHapley Additive exPlanations) is a unified approach to explain the output of any machine learning model.
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.
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.
auto-shap · PyPI
https://pypi.org/project/auto-shapauto-shap. The auto-shap library is your best friend when calculating SHAP values! SHAP is a state-of-the-art technique for explaining model predictions. Model explanation can be valuable in many regards. For one, understanding how a model devised a prediction can engender trust.
SHAP: How to Interpret Machine Learning Models With Python
betterdatascience.com › shapNov 09, 2020 · Model training. To interpret a machine learning model, we first need a model — so let’s create one based on the Wine quality dataset. Here’s how to load it into Python: import pandas as pd wine = pd. read_csv ( 'wine.csv') wine. head () Wine dataset head (image by author) There’s no need for data cleaning — all data types are numeric ...