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?
• Estimating local and global feature importance scores using DiCE
  • Preliminaries: Loading the data and ML model
  • Local feature importance
  • Global importance
  • Convert the counterfactual output to json
  • Convert the json output to a counterfactual object
• Generating counterfactuals for multi-class classification and regression models
  • Multiclass Classification
• Regression
• Generating counterfactual explanations with any ML model
  • 1. Independent random sampling of features
  • 2. Genetic Algorithm
  • 3. Querying a KD Tree
• Generating counterfactual explanations without access to training data
  • Defining meta data
  • Generate diverse counterfactuals
  • 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