DiCE

Getting Started:

  • Diverse Counterfactual Explanations (DiCE) for ML

Notebooks:

  • Quick introduction to generating counterfactual explanations using DiCE
  • Estimating local and global feature importance scores using DiCE
  • Generating counterfactuals for multi-class classification and regression models
  • Regression
  • Generating counterfactual explanations with any ML model
  • Generating counterfactual explanations without access to training data
  • Advanced options to customize Counterfactual Explanations

Package:

  • dice_ml package
DiCE
  • »
  • Overview: module code

All modules for which code is available

  • dice_ml.constants
  • dice_ml.counterfactual_explanations
  • dice_ml.data
  • dice_ml.data_interfaces.private_data_interface
  • dice_ml.data_interfaces.public_data_interface
  • dice_ml.dice
  • dice_ml.diverse_counterfactuals
  • dice_ml.explainer_interfaces.dice_KD
  • dice_ml.explainer_interfaces.dice_genetic
  • dice_ml.explainer_interfaces.dice_pytorch
  • dice_ml.explainer_interfaces.dice_random
  • dice_ml.explainer_interfaces.dice_tensorflow1
  • dice_ml.explainer_interfaces.dice_tensorflow2
  • dice_ml.explainer_interfaces.explainer_base
  • dice_ml.explainer_interfaces.feasible_base_vae
  • dice_ml.explainer_interfaces.feasible_model_approx
  • dice_ml.model
  • dice_ml.model_interfaces.base_model
  • dice_ml.model_interfaces.keras_tensorflow_model
  • dice_ml.model_interfaces.pytorch_model
  • dice_ml.utils.exception
  • dice_ml.utils.helpers
  • dice_ml.utils.neuralnetworks
  • dice_ml.utils.sample_architecture.vae_model
  • dice_ml.utils.serialize

© Copyright 2020, Ramaravind, Amit, Chenhao.

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