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  • Overview
    • Introduction
    • Getting Started
    • Algorithm Overview
    • White-box and black-box models
    • Saving and loading
    • Frequently Asked Questions
  • Explanations
    • Methods
      • ALE
      • Anchors
      • CEM
      • CF
      • CFProto
      • CFRL
      • IntegratedGradients
      • KernelSHAP
      • LinearityMeasure
      • PartialDependence
      • PartialDependenceVariance
      • PermutationImportance
      • ProtoSelect
      • Similarity
      • TreeSHAP
      • TrustScores
    • Examples
      • Alibi Overview Examples
      • Accumulated Local Effets
        • 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 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
  • Model Confidence
    • Methods
      • Measuring the linearity of machine learning models
      • Trust Scores
    • Examples
      • Measuring the linearity of machine learning models
        • Linearity measure applied to fashion MNIST
        • Linearity measure applied to Iris
      • Trust Scores
        • Trust Scores applied to Iris
        • Trust Scores applied to MNIST
  • Prototypes
    • Methods
      • ProtoSelect
    • Examples
      • ProtoSelect on Adult Census and CIFAR10
  • API Reference
    • alibi.api
      • alibi.api.defaults
      • alibi.api.interfaces
    • alibi.confidence
      • alibi.confidence.model_linearity
      • alibi.confidence.trustscore
    • alibi.datasets
      • alibi.datasets.default
      • alibi.datasets.tensorflow
    • alibi.exceptions
    • alibi.explainers
      • alibi.explainers.ale
      • alibi.explainers.anchors
        • alibi.explainers.anchors.anchor_base
        • alibi.explainers.anchors.anchor_explanation
        • alibi.explainers.anchors.anchor_image
        • alibi.explainers.anchors.anchor_tabular
        • alibi.explainers.anchors.anchor_tabular_distributed
        • alibi.explainers.anchors.anchor_text
        • alibi.explainers.anchors.language_model_text_sampler
        • alibi.explainers.anchors.text_samplers
      • alibi.explainers.backends
        • alibi.explainers.backends.cfrl_base
        • alibi.explainers.backends.cfrl_tabular
        • alibi.explainers.backends.pytorch
          • alibi.explainers.backends.pytorch.cfrl_base
          • alibi.explainers.backends.pytorch.cfrl_tabular
        • alibi.explainers.backends.tensorflow
          • alibi.explainers.backends.tensorflow.cfrl_base
          • alibi.explainers.backends.tensorflow.cfrl_tabular
      • alibi.explainers.cem
      • alibi.explainers.cfproto
      • alibi.explainers.cfrl_base
      • alibi.explainers.cfrl_tabular
      • alibi.explainers.counterfactual
      • alibi.explainers.integrated_gradients
      • alibi.explainers.partial_dependence
      • alibi.explainers.pd_variance
      • alibi.explainers.permutation_importance
      • alibi.explainers.shap_wrappers
      • alibi.explainers.similarity
        • alibi.explainers.similarity.backends
          • alibi.explainers.similarity.backends.pytorch
            • alibi.explainers.similarity.backends.pytorch.base
          • alibi.explainers.similarity.backends.tensorflow
            • alibi.explainers.similarity.backends.tensorflow.base
        • alibi.explainers.similarity.base
        • alibi.explainers.similarity.grad
        • alibi.explainers.similarity.metrics
    • alibi.models
      • alibi.models.pytorch
        • alibi.models.pytorch.actor_critic
        • alibi.models.pytorch.autoencoder
        • alibi.models.pytorch.cfrl_models
        • alibi.models.pytorch.metrics
        • alibi.models.pytorch.model
      • alibi.models.tensorflow
        • alibi.models.tensorflow.actor_critic
        • alibi.models.tensorflow.autoencoder
        • alibi.models.tensorflow.cfrl_models
    • alibi.prototypes
      • alibi.prototypes.protoselect
    • alibi.saving
    • alibi.utils
      • alibi.utils.approximation_methods
      • alibi.utils.data
      • alibi.utils.discretizer
      • alibi.utils.distance
      • alibi.utils.distributed
      • alibi.utils.distributions
      • alibi.utils.download
      • alibi.utils.frameworks
      • alibi.utils.gradients
      • alibi.utils.kernel
      • alibi.utils.lang_model
      • alibi.utils.mapping
      • alibi.utils.missing_optional_dependency
      • alibi.utils.tf
      • alibi.utils.visualization
      • alibi.utils.wrappers
    • alibi.version
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  1. Explanations
  2. Examples

Tree SHAP

Explaining Tree Models with Interventional Feature Perturbation Tree SHAPExplaining Tree Models with Path-Dependent Feature Perturbation Tree SHAP
PreviousSimilarity explanations for MNISTNextExplaining Tree Models with Interventional Feature Perturbation Tree SHAP

Last updated 24 days ago

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