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

arXiv:2404.07046v1 Announce Type: new
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 …

abstract act arxiv comparison cs.ai cs.lg decision decision trees error global lime linear linear regression mean model-agnostic regression square support terms trees type values vector work