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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

Model Confidence

  • Methods
  • Examples

Prototypes

  • Methods
  • Examples

API reference

  • API reference
Alibi
  • Alibi Explain
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Alibi Logo

Alibi Explain

Alibi Explain is an open source Python library aimed at machine learning model inspection and interpretation. The focus of the library is to provide high-quality implementations of black-box, white-box, local and global explanation methods for classification and regression models.

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
    • Alibi Overview Example
    • 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

Model Confidence

  • Methods
    • Measuring the linearity of machine learning models
    • Trust Scores
  • Examples
    • Measuring the linearity of machine learning models
    • Trust Scores

Prototypes

  • Methods
    • ProtoSelect
  • Examples
    • ProtoSelect

API reference

  • API reference
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© Copyright 2019, Seldon Technologies Ltd. Revision 43a3354c.

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