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    • Introduction
    • Getting Started
    • Algorithm Overview
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    • Saving and loading
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  • Explanations
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    • 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
      • Partial Dependence
      • Partial Dependence Variance
      • Permutation Importance
        • Permutation Feature Importance on “Who’s Going to Leave Next?”
      • Similarity explanations
      • Tree SHAP
  • Model Confidence
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    • Examples
  • Prototypes
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  • 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

Permutation Importance

Permutation Feature Importance on “Who’s Going to Leave Next?”
PreviousFeature importance and feature interaction based on partial dependece varianceNextPermutation Feature Importance on “Who’s Going to Leave Next?”

Last updated 2 months ago

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