Siamese Networks
AI Infrastructure
A method that calculates how much each feature contributes to a model’s prediction.
Detailed Explanation
SHAP Values (SHapley Additive exPlanations) quantify the contribution of each feature to a specific prediction, based on game theory. They help interpret complex models by providing consistent, fair attribution scores, enabling understanding of how individual features influence the output. This transparency improves model trust, debugging, and feature importance analysis within AI infrastructure.
Use Cases
- •Use case: Explaining individual model predictions to improve trust and debugging in credit scoring systems.
Related Terms
Other terms in the AI Infrastructure category