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Overview
Getting Started
Algorithm overview
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
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
API reference
API reference
alibi package
Subpackages
alibi.api package
alibi.confidence package
alibi.explainers package
alibi.tests package
alibi.utils package
Submodules
Alibi
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alibi.tests package
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alibi.tests package
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alibi.tests.utils module
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