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Overview

  • Getting Started
  • Algorithm overview
  • Roadmap

Methods

  • Anchors
  • Contrastive Explanation Method
  • Counterfactual Instances
  • Counterfactuals Guided by Prototypes
  • Kernel SHAP
  • Measuring the linearity of machine learning models
  • Trust Scores

Examples

  • Anchor explanations for income prediction
  • Anchor explanations on the Iris dataset
  • Anchor explanations for movie sentiment
  • Anchor explanations for ImageNet
  • Anchor explanations for fashion MNIST
  • Contrastive Explanations Method (CEM) applied to MNIST
  • Contrastive Explanations Method (CEM) applied to Iris dataset
  • Counterfactual instances on MNIST
  • Counterfactuals guided by prototypes on MNIST
  • Counterfactuals guided by prototypes on Boston housing dataset
  • Counterfactual explanations with one-hot encoded categorical variables
  • Counterfactual explanations with ordinally encoded categorical variables
  • Kernel SHAP explanation for SVM models
  • Kernel SHAP explanation for multinomial logistic regression models
  • Handling categorical variables with KernelSHAP
  • KernelSHAP: combining preprocessor and predictor
  • Linearity measure applied to Iris
  • Linearity measure applied to fashion MNIST
  • Trust Scores applied to Iris
  • Trust Scores applied to MNIST

API reference

  • API reference
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