DiCE
Getting Started:
Diverse Counterfactual Explanations (DiCE) for ML
Notebooks:
Quick introduction to generating counterfactual explanations using DiCE
Defining meta data
Loading trained ML model
Generate diverse counterfactuals
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
Preliminaries: Loading a dataset and a ML model trained over it
Generating counterfactual examples using DiCE
Generating feature attributions (local and global) using DiCE
Working with deep learning models (TensorFlow and PyTorch)
More resources: What’s next?
Defining meta data
Loading trained ML model
Generate diverse counterfactuals
Advanced options to customize Counterfactual Explanations
Loading dataset
1. Loading a custom ML model
Generate diverse counterfactuals
2. Changing feature weights
3. Trading off between proximity and diversity goals
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