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
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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