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Overview

  • Getting Started
  • Algorithm overview
  • Saving and loading
  • Roadmap

Methods

  • Accumulated Local Effects
  • Anchors
  • Contrastive Explanation Method
  • Counterfactual Instances
  • Counterfactuals Guided by Prototypes
  • Integrated Gradients
  • Kernel SHAP
  • Measuring the linearity of machine learning models
  • Trust Scores
  • Tree SHAP

Examples

  • Accumulated Local Effects for predicting house prices
  • Accumulated Local Effects for classifying flowers
  • 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
  • Distributed KernelSHAP
  • Linearity measure applied to Iris
  • Linearity measure applied to fashion MNIST
  • Trust Scores applied to Iris
  • Trust Scores applied to MNIST
  • Explaining Tree Models with Interventional Feature Perturbation Tree SHAP
  • Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP
  • Integrated gradients for a ResNet model trained on Imagenet dataset
  • Integrated gradients for MNIST
  • Integrated gradients for text classification on the IMDB dataset
  • Integrated gradients for transformers models

API reference

  • API reference
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