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  • Welcome to the SHAP documentation
    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)
  • API Reference — SHAP latest documentation - Read the Docs
    shap plots scatter (shap_values[, color, ]) Create a SHAP dependence scatter plot, optionally colored by an interaction feature shap plots heatmap (shap_values[, ])
  • shap. Explainer — SHAP latest documentation - Read the Docs
    This is the primary explainer interface for the SHAP library It takes any combination of a model and masker and returns a callable subclass object that implements the particular estimation algorithm that was chosen
  • decision plot — SHAP latest documentation - Read the Docs
    Decision plots support SHAP interaction values: the first-order interactions estimated from tree-based models While SHAP dependence plots are the best way to visualize individual interactions, a decision plot can display the cumulative effect of main effects and interactions for one or more observations
  • Topical Overviews — SHAP latest documentation - Read the Docs
    Topical Overviews These overviews are generated from Jupyter notebooks that are available on GitHub
  • Explaining quantitative measures of fairness — SHAP latest documentation
    By using SHAP (a popular explainable AI tool) we can decompose measures of fairness and allocate responsibility for any observed disparity among each of the model’s input features Explaining these quantitative fairness metrics can reduce the concerning tendency to rely on them as opaque standards of fairness, and instead promote their
  • shap. Explanation — SHAP latest documentation - Read the Docs
    A sliceable set of parallel arrays representing a SHAP explanation Notes The instance methods such as max() return new Explanation objects with the operation applied The class methods such as Explanation max return OpChain objects that represent a set of dot chained operations without actually running them
  • Text examples — SHAP latest documentation - Read the Docs
    Text examples These examples explain machine learning models applied to text data They are all generated from Jupyter notebooks available on GitHub Sentiment analysis
  • shap. DeepExplainer — SHAP latest documentation - Read the Docs
    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





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