XAI | Jannik P. Roth

Protocol to calculate and compare exact Shapley values for different kernels in support vector machine models using binary features

A protocol to calculate and compare exact Shapley values for support vector machine models with commonly used kernels and binary input features is developed.

Workflow of the porposed method, after the activity prediction, Shapley Values are calculated which are then mapped to chemical substructures.

Machine learning models with distinct Shapley value explanations decouple feature attribution and interpretation for chemical compound predictions

A method for the exact calculation of Shapley Values for Support Vector Machines is introduced and tested for compound prediction tasks.