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# Table of contents
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* [README](README.md)
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## Overview
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* [Introduction](source/overview/high_level.md)
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* [Introduction](source/overview/README.md)
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* [Getting Started](source/overview/getting_started.md)
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* [Algorithm Overview](source/overview/algorithms.md)
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* [White-box and black-box models](source/overview/white_box_black_box.md)
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* [Saving and loading](source/overview/saving.md)
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* [Frequently Asked Questions](source/overview/faq.md)
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## Explanations
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* [Methods](source/methods/README.md)
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* [ALE](source/methods/ale.md)
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* [Anchors](source/methods/anchors.md)
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* [CEM](source/methods/cem.md)
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* [CF](source/methods/cf.md)
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* [CFProto](source/methods/cfproto.md)
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* [CFRL](source/methods/cfrl.md)
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* [IntegratedGradients](source/methods/integratedgradients.md)
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* [KernelSHAP](source/methods/kernelshap.md)
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* [LinearityMeasure](source/methods/linearitymeasure.md)
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* [PartialDependence](source/methods/partialdependence.md)
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* [PartialDependenceVariance](source/methods/partialdependencevariance.md)
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* [PermutationImportance](source/methods/permutationimportance.md)
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* [ProtoSelect](source/methods/protoselect.md)
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* [Similarity](source/methods/similarity.md)
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* [TreeSHAP](source/methods/treeshap.md)
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* [TrustScores](source/methods/trustscores.md)
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* [Examples](source/methods/README.md)
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* [Alibi Overview Examples](source/examples/overview.md)
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* [Accumulated Local Effets]
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* [Explanations](source/explanations/README.md)
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* [Examples](source/explanations/examples.md)
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* [Methods](source/explanations/methods.md)
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* [methods](source/methods/README.md)
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* [ALE](source/methods/ale.md)
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* [Anchors](source/methods/anchors.md)
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* [CEM](source/methods/cem.md)
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* [CF](source/methods/cf.md)
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* [CFProto](source/methods/cfproto.md)
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* [CFRL](source/methods/cfrl.md)
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* [IntegratedGradients](source/methods/integratedgradients.md)
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* [KernelSHAP](source/methods/kernelshap.md)
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* [LinearityMeasure](source/methods/linearitymeasure.md)
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* [PartialDependence](source/methods/partialdependence.md)
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* [PartialDependenceVariance](source/methods/partialdependencevariance.md)
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* [PermutationImportance](source/methods/permutationimportance.md)
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* [ProtoSelect](source/methods/protoselect.md)
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* [Similarity](source/methods/similarity.md)
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* [TreeSHAP](source/methods/treeshap.md)
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* [TrustScores](source/methods/trustscores.md)
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* [ALE Figures](source/methods/ale_figures.md)
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## Model Confidence
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* [confidence](source/confidence/README.md)
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* [Examples](source/confidence/examples.md)
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* [Methods](source/confidence/methods.md)
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* [examples](source/examples/README.md)
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* [Accumulated Local Effects for classifying flowers](source/examples/ale_classification.md)
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* [Accumulated Local Effects for predicting house prices](source/examples/ale_regression_california.md)
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* [Anchors]
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* [Anchor explanations for fashion MNIST](source/examples/anchor_image_fashion_mnist.md)
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* [Anchor explanations for ImageNet](source/examples/anchor_image_imagenet.md)
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* [Anchor explanations for income prediction](source/examples/anchor_tabular_adult.md)
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* [Anchor explanations on the Iris dataset](source/examples/anchor_tabular_iris.md)
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* [Anchor explanations for movie sentiment](source/examples/anchor_text_movie.md)
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* [Contrastive Explanation Method]
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* [Contrastive Explanations Method (CEM) applied to Iris dataset](source/examples/cem_iris.md)
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* [Contrastive Explanations Method (CEM) applied to MNIST](source/examples/cem_mnist.md)
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* [Counterfactual Instances on MNIST](source/examples/cf_mnist.md)
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* [Counterfactuals Guided by Prototypes]
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* [Counterfactual instances on MNIST](source/examples/cf_mnist.md)
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* [Counterfactual explanations with one-hot encoded categorical variables](source/examples/cfproto_cat_adult_ohe.md)
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* [Counterfactual explanations with ordinally encoded categorical variables](source/examples/cfproto_cat_adult_ord.md)
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* [Counterfactuals guided by prototypes on California housing dataset](source/examples/cfproto_housing.md)
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* [Counterfactuals guided by prototypes on MNIST](source/examples/cfproto_mnist.md)
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* [Counterfactuals with Reinforcement Learning]
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* [Counterfactual with Reinforcement Learning (CFRL) on Adult Census](source/examples/cfrl_adult.md)
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* [Counterfactual with Reinforcement Learning (CFRL) on MNIST](source/examples/cfrl_mnist.md)
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* [Integrated Gradients]
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* [Distributed KernelSHAP](source/examples/distributed_kernel_shap_adult_lr.md)
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* [Integrated gradients for a ResNet model trained on Imagenet dataset](source/examples/integrated_gradients_imagenet.md)
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* [Integrated gradients for text classification on the IMDB dataset](source/examples/integrated_gradients_imdb.md)
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* [Integrated gradients for MNIST](source/examples/integrated_gradients_mnist.md)
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* [Integrated gradients for transformers models](source/examples/integrated_gradients_transformers.md)
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* [Kernel SHAP]
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* [Distributed KernelSHAP](source/examples/distributed_kernel_shap_adult_lr.md)
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* [Explaining Tree Models with Interventional Feature Perturbation Tree SHAP](source/examples/interventional_tree_shap_adult_xgb.md)
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* [KernelSHAP: combining preprocessor and predictor](source/examples/kernel_shap_adult_categorical_preproc.md)
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* [Handling categorical variables with KernelSHAP](source/examples/kernel_shap_adult_lr.md)
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* [Kernel SHAP explanation for SVM models](source/examples/kernel_shap_wine_intro.md)
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* [Kernel SHAP explanation for multinomial logistic regression models](source/examples/kernel_shap_wine_lr.md)
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* [Partial Dependence]
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* [Partial Dependence and Individual Conditional Expectation for predicting bike renting](source/examples/pdp_regression_bike.md)
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* [Partial Dependence Variance]
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* [Linearity measure applied to fashion MNIST](source/examples/linearity_measure_fashion_mnist.md)
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* [Linearity measure applied to Iris](source/examples/linearity_measure_iris.md)
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* [Alibi Overview Example](source/examples/overview.md)
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* [Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP](source/examples/path_dependent_tree_shap_adult_xgb.md)
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* [Feature importance and feature interaction based on partial dependece variance](source/examples/pd_variance_regression_friedman.md)
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* [Permutation Importance]
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* [Permutation Feature Importance on Whos Going to Leave Next?](source/examples/permutation_importance_classification_leave.md)
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* [Similarity explanations]
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* [Partial Dependence and Individual Conditional Expectation for predicting bike renting](source/examples/pdp_regression_bike.md)
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* [Permutation Feature Importance on "Who's Going to Leave Next?"](source/examples/permutation_importance_classification_leave.md)
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* [ProtoSelect on Adult Census and CIFAR10](source/examples/protoselect_adult_cifar10.md)
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* [Similarity explanations for 20 newsgroups dataset](source/examples/similarity_explanations_20ng.md)
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* [Similarity explanations for ImageNet](source/examples/similarity_explanations_imagenet.md)
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* [Similarity explanations for MNIST](source/examples/similarity_explanations_mnist.md)
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* [Tree SHAP]
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* [Explaining Tree Models with Interventional Feature Perturbation Tree SHAP](source/examples/interventional_tree_shap_adult_xgb.md)
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* [Explaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP](source/examples/path_dependent_tree_shap_adult_xgb.md)
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## Model Confidence
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* [Methods]
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* [Measuring the linearity of machine learning models](source/methods/linearitymeasure.md)
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* [Trust Scores](source/methods/trustscores.md)
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* [Examples]
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* [Measuring the linearity of machine learning models]
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* [Linearity measure applied to fashion MNIST](source/examples/linearity_measure_fashion_mnist.md)
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* [Linearity measure applied to Iris](source/examples/linearity_measure_iris.md)
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* [Trust Scores]
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* [Trust Scores applied to Iris](source/examples/trustscore_iris.md)
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* [Trust Scores applied to MNIST](source/examples/trustscore_mnist.md)
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* [A Gradient Boosted Tree Model for the Adult Dataset](source/examples/xgboost_model_fitting_adult.md)
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* [Methods](model-confidence/confidence/methods-1.md)
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* [Examples](model-confidence/confidence/examples-2.md)
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## Prototypes
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* [prototypes](source/prototypes/README.md)
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* [Methods](source/prototypes/methods.md)
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* [Examples](source/prototypes/examples.md)
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* [Methods](source/prototypes/methods.md)
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## API Reference
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