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Variation

Variation

An important characteristic of any set of data is the variation in the data. In some data sets, the data values are concentrated closely near the mean; in other data sets, the data values are more widely spread out from the mean. The most common measure of variation, or spread, is the standard deviation, which is the square root of variance.
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Protocol to explain support vector machine predictions via exact Shapley value computation.

Andrea Mastropietro¹, Jürgen Bajorath²

¹Deparment of Computer, Control and Management Engineering "Antonio Ruberti", Sapienza University of Rome, Via Ariosto 25, 00185 Rome, Italy.

|April 12, 2024

Summary

This summary is machine-generated.

This study introduces a protocol for precisely calculating Shapley values to explain machine learning predictions, specifically for support vector machines. The method offers quantitative feature analysis and visualization for better model interpretability.

Keywords:

Bioinformatics Chemistry Computer sciences

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Area of Science:

Computational chemistry
Machine learning
Cheminformatics

Background:

Shapley values are commonly used to explain machine learning (ML) predictions but are often approximated for large feature sets.
Exact computation of Shapley values is computationally intensive, especially for complex models like support vector machines (SVMs).

Purpose of the Study:

To present a protocol for exact Shapley value computation to explain SVM predictions.
To provide practical tools and methods for applying these algorithms.
To enable quantitative feature analysis and visualization of important features.

Main Methods:

Developed a protocol for two techniques enabling exact Shapley value computation for SVMs.
Provided ready-to-use Python scripts and custom code for implementation.
Focused on explaining predictions for large feature sets in ML.

Main Results:

The protocol allows for precise Shapley value calculation, overcoming approximation limitations.
Generated quantitative feature analysis and feature importance mapping for visualization.
Demonstrated the application of exact Shapley value computation for SVMs.

Conclusions:

Exact Shapley value computation is feasible and beneficial for explaining SVM predictions.
The provided protocol and scripts facilitate accurate feature analysis and model interpretability.
This approach enhances understanding of feature contributions in ML models.