Visualisation and interpretation of Support Vector Regression models.

Area of Science:

• Chemometrics
• Machine Learning
• Data Visualization

Background:

• Support Vector Machines (SVM) are increasingly used in chemistry and chemometrics for classification (SVC) and regression (SVR) due to their generalization performance and ability to model non-linear relationships.
• Kernel functions transform data into higher dimensions, enabling linear representation of non-linear relationships.
• SVM models are often treated as 'black boxes,' lacking the interpretability of methods like Partial Least Squares (PLS).

Purpose of the Study:

• To develop techniques for visualizing the information content of the kernel matrix in Support Vector Regression (SVR).
• To provide a method for interpreting the components of an SVR model.
• To enhance the transparency and interpretability of SVR models in regression tasks.

Main Methods:

• Investigation of visualization techniques for the kernel matrix in SVR.
• Development of methods to interpret the internal workings and parameters of SVR models.
• Focus on Support Vector Regression (SVR) to demonstrate the proposed interpretation and visualization approaches.

Main Results:

• A novel technique to visualize the information content of the SVR kernel matrix has been introduced.
• A method for interpreting the individual components and parameters within an SVR model has been established.
• The study successfully demonstrates the transformation of SVR from a black box to an interpretable regression technique.

Conclusions:

• The developed techniques enable the visualization and interpretation of Support Vector Regression (SVR) models.
• This work makes SVR a more transparent and understandable regression modeling technique.
• The findings contribute to the broader adoption and understanding of SVMs in scientific applications.