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
<|reference_start|>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: 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.<|reference_end|>
@article{thombre2024comparison,
    title={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},
    author={Amit Thombre},
    journal={arXiv preprint arXiv:2404.07046},
    year={2024},
    archivePrefix={arXiv},
    eprint={2404.07046},
    primaryClass={cs.LG cs.AI}
}