Protocol to explain support vector machine predictions via exact Shapley value computation
Summary: Shapley values from cooperative game theory are adapted for explaining machine learning predictions. For large feature sets used in machine learning, Shapley values are approximated. We present a protocol for two techniques for explaining support vector machine predictions with exact Shaple...
Main Authors: | Andrea Mastropietro, Jürgen Bajorath |
---|---|
Format: | Article |
Language: | English |
Published: |
Elsevier
2024-06-01
|
Series: | STAR Protocols |
Subjects: | |
Online Access: | http://www.sciencedirect.com/science/article/pii/S2666166724001758 |
Similar Items
-
Protocol to explain graph neural network predictions using an edge-centric Shapley value-based approach
by: Andrea Mastropietro, et al.
Published: (2022-12-01) -
Calculation of exact Shapley values for explaining support vector machine models using the radial basis function kernel
by: Andrea Mastropietro, et al.
Published: (2023-11-01) -
Calculation of exact Shapley values for support vector machines with Tanimoto kernel enables model interpretation
by: Christian Feldmann, et al.
Published: (2022-09-01) -
EdgeSHAPer: Bond-centric Shapley value-based explanation method for graph neural networks
by: Andrea Mastropietro, et al.
Published: (2022-10-01) -
Explaining Multiclass Compound Activity Predictions Using Counterfactuals and Shapley Values
by: Alec Lamens, et al.
Published: (2023-07-01)