DiCE

Getting Started:

  • Diverse Counterfactual Explanations (DiCE) for ML

Notebooks:

  • Quick introduction to generating counterfactual explanations using DiCE
  • Generating Diverse Counterfactual Explanations without accessing training data
  • Advanced options to customize Counterfactual Explanations
  • Generate feasible counterfactual explanations using a VAE
  • Adding feasibility constraints

Package:

  • dice_ml package
DiCE
  • Docs »
  • <no title>
  • View page source

Example notebooks

Notebooks:

  • Quick introduction to generating counterfactual explanations using DiCE
    • Loading dataset
    • Loading the ML model
    • Generate diverse counterfactuals
    • Working with PyTorch
  • Generating Diverse Counterfactual Explanations without accessing training data
    • Defining meta data
    • Loading trained ML model
    • Generate diverse counterfactuals
  • Advanced options to customize Counterfactual Explanations
    • Loading dataset
    • 1. Training a custom ML model
    • Generate diverse counterfactuals
    • 2. Changing feature weights
    • 3. Trading off between proximity and diversity goals
    • 4. Selecting the features to vary
  • Generate feasible counterfactual explanations using a VAE
    • Loading dataset
    • Loading the ML model
    • Generate counterfactuals using a VAE model
  • Adding feasibility constraints
    • ModelApprox
    • Initilize the Model and Explainer for FeasibleModelApprox
Next Previous

© Copyright 2020, Ramaravind, Amit, Chenhao

Built with Sphinx using a theme provided by Read the Docs.