Examples

• Alibi Overview Example

Accumulated Local Effects

• Accumulated Local Effects for classifying flowers
• Accumulated Local Effects for predicting house prices

Anchors

• Anchor explanations for fashion MNIST
• Anchor explanations for ImageNet
• Anchor explanations for income prediction
• Anchor explanations on the Iris dataset
• Anchor explanations for movie sentiment

Contrastive Explanation Method

• Contrastive Explanations Method (CEM) applied to Iris dataset
• Contrastive Explanations Method (CEM) applied to MNIST

Counterfactual Instances

• Counterfactual instances on MNIST

Counterfactuals Guided by Prototypes

• Counterfactual explanations with one-hot encoded categorical variables
• Counterfactual explanations with ordinally encoded categorical variables
• Counterfactuals guided by prototypes on California housing dataset
• Counterfactuals guided by prototypes on MNIST

Counterfactuals with Reinforcement Learning

• Counterfactual with Reinforcement Learning (CFRL) on Adult Census
• Counterfactual with Reinforcement Learning (CFRL) on MNIST

Integrated Gradients

• Integrated gradients for a ResNet model trained on Imagenet dataset
• Integrated gradients for text classification on the IMDB dataset
• Integrated gradients for MNIST
• Integrated gradients for transformers models

Kernel SHAP

• Distributed KernelSHAP
• KernelSHAP: combining preprocessor and predictor
• Handling categorical variables with KernelSHAP
• Kernel SHAP explanation for SVM models
• Kernel SHAP explanation for multinomial logistic regression models

Partial Dependence

• Partial Dependence and Individual Conditional Expectation for predicting bike renting

Partial Dependence Variance

• Feature importance and feature interaction based on partial dependece variance

Permutation Importance

• Permutation Feature Importance on “Who’s Going to Leave Next?”

Similarity explanations

• Similarity explanations for 20 newsgroups dataset
• Similarity explanations for ImageNet
• Similarity explanations for MNIST

Tree SHAP

• Explaining Tree Models with Interventional Feature Perturbation Tree SHAP
• Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP