Comparison of decision trees with Local Interpretable Model-Agnostic Explanations (LIME) technique and multi-linear regression for explaining support vector regression model in terms of root mean square error (RMSE) values

TL;DR

This paper compares decision trees, LIME, and multi-linear regression in explaining support vector regression models, finding decision trees often provide lower RMSE and better local explanations than LIME.

Contribution

It introduces a comparative analysis of decision trees, LIME, and multi-linear regression for explaining support vector regression, highlighting decision trees' superior performance.

Findings

Decision trees yield lower RMSE than LIME in 87% of runs.

Decision trees outperform LIME in local explanations with statistically significant results.

Multi-linear regression performs better than LIME in most cases but without statistical significance.

Abstract

In this work the decision trees are used for explanation of support vector regression model. The decision trees act as a global technique as well as a local technique. They are compared against the popular technique of LIME which is a local explanatory technique and with multi linear regression. It is observed that decision trees give a lower RMSE value when fitted to support vector regression as compared to LIME in 87% of the runs over 5 datasets. The comparison of results is statistically significant. Multi linear regression also gives a lower RMSE value when fitted to support vector regression model as compared to LIME in 73% of the runs over 5 datasets but the comparison of results is not statistically significant. Also, when used as a local explanatory technique, decision trees give better performance than LIME and the comparison of results is statistically significant.