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Overview

  • Introduction
  • Getting Started
  • Algorithm overview
  • White-box and black-box models
  • Saving and loading
  • Frequently Asked Questions

Explanations

  • Methods
    • Accumulated Local Effects
    • Anchors
    • Contrastive Explanation Method
    • Counterfactual Instances
    • Counterfactuals Guided by Prototypes
    • Counterfactuals with Reinforcement Learning
    • Integrated Gradients
    • Kernel SHAP
    • Partial Dependence
    • Partial Dependence Variance
    • Permutation Importance
    • Similarity explanations
    • Tree SHAP
  • Examples

Model Confidence

  • Methods
  • Examples

Prototypes

  • Methods
  • Examples

API reference

  • API reference
Alibi
  • Methods
  • Edit on GitHub

Methods

  • Accumulated Local Effects
  • Anchors
  • Contrastive Explanation Method
  • Counterfactual Instances
  • Counterfactuals Guided by Prototypes
  • Counterfactuals with Reinforcement Learning
  • Integrated Gradients
  • Kernel SHAP
  • Partial Dependence
  • Partial Dependence Variance
  • Permutation Importance
  • Similarity explanations
  • Tree SHAP
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