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  • An introduction to explainable AI with Shapley values — SHAP latest . . .
    An introduction to explainable AI with Shapley values This is an introduction to explaining machine learning models with Shapley values Shapley values are a widely used approach from cooperative game theory that come with desirable properties This tutorial is designed to help build a solid understanding of how to compute and interpet Shapley-based explanations of machine learning models We
  • Image examples — SHAP latest documentation
    Image examples These examples explain machine learning models applied to image data They are all generated from Jupyter notebooks available on GitHub Image classification Examples using shap explainers Partition to explain image classifiers
  • decision plot — SHAP latest documentation
    1 SHAP Decision Plots 1 1 Load the dataset and train the model 1 2 Calculate SHAP values 2 Basic decision plot features 3 When is a decision plot helpful? 3 1 Show a large number of feature effects clearly 3 2 Visualize multioutput predictions 3 3 Display the cumulative effect of interactions 3 4 Explore feature effects for a range of feature values 3 5 Identify outliers 3 6 Identify typical
  • shap. TreeExplainer — SHAP latest documentation
    Tree SHAP is a fast and exact method to estimate SHAP values for tree models and ensembles of trees, under several different possible assumptions about feature dependence
  • shap. DeepExplainer — SHAP latest documentation
    shap DeepExplainer class shap DeepExplainer(model, data, session=None, learning_phase_flags=None) Meant to approximate SHAP values for deep learning models This is an enhanced version of the DeepLIFT algorithm (Deep SHAP) where, similar to Kernel SHAP, we approximate the conditional expectations of SHAP values using a selection of background samples Lundberg and Lee, NIPS 2017 showed that
  • Tabular examples — SHAP latest documentation
    Tabular examples These examples explain machine learning models applied to tabular data They are all generated from Jupyter notebooks available on GitHub Tree-based models Examples demonstrating how to explain tree-based machine learning models
  • Release notes — SHAP latest documentation
    Release notes To see the latest changes that are due on the next release, see v0 51 0…master v0 51 0 Released on 2026-03-04 - GitHub - PyPI What's Changed Fixes fix: check first that the feature is not in the leaf node by @Far-naz in #4268 Fix missing array to scalar conversion in MAPLE by @Scienfitz in #4285 fix Python Version of Tree SHAP notebook by @CloseChoice in #4289 Fix path





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