Description of Problem:

• PyTorch is a great multi-purpose neural network development framework.
• Deep learning algorithms are getting easier to train, understand, deploy and manage and can be used with great efficiency in enterprise contexts.
• SHAP values can be computed for neural nets and serve for model explainability purposes.

Overview of the Solution:

• Integrate the DeepLIFT algorithm (Deep SHAP), and the possibility to visualize the estimated values in the SHAPASH GUI.

Examples:

• Attribution algorithms available in the Captum Python lib
  • SHAP
  • LIME

Blockers:

• Not all explainability methods will be suitable, this is a limitation rather than a blocker

Definition of Done:

• PyTorch models trained on tabular data (regression or classification use cases) can be used with SHAPASH