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  • Overview
    • Introduction
    • Getting Started
    • Algorithm Overview
    • White-box and black-box models
    • Saving and loading
    • Frequently Asked Questions
  • Explanations
    • Methods
    • Examples
      • Alibi Overview Examples
      • Accumulated Local Effets
      • Anchors
      • Contrastive Explanation Method
      • Counterfactual Instances on MNIST
      • Counterfactuals Guided by Prototypes
      • Counterfactuals with Reinforcement Learning
      • Integrated Gradients
      • Kernel SHAP
        • Distributed KernelSHAP
        • KernelSHAP: combining preprocessor and predictor
        • Handling categorical variables with KernelSHAP
        • Kernel SHAP explanation for SVM models
        • Kernel SHAP explanation for multinomial logistic regression models
      • Partial Dependence
      • Partial Dependence Variance
      • Permutation Importance
      • Similarity explanations
      • Tree SHAP
  • Model Confidence
    • Methods
    • Examples
  • Prototypes
    • Methods
    • Examples
  • API Reference
    • alibi.api
    • alibi.confidence
    • alibi.datasets
    • alibi.exceptions
    • alibi.explainers
    • alibi.models
    • alibi.prototypes
    • alibi.saving
    • alibi.utils
    • alibi.version
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  1. Explanations
  2. Examples

Kernel SHAP

Distributed KernelSHAPKernelSHAP: combining preprocessor and predictorHandling categorical variables with KernelSHAPKernel SHAP explanation for SVM modelsKernel SHAP explanation for multinomial logistic regression models
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Last updated 2 months ago

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