Summary of 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, by Amit Thombre
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
by Amit Thombre
First submitted to arxiv on: 10 Apr 2024
Categories
- Main: Machine Learning (cs.LG)
- Secondary: Artificial Intelligence (cs.AI)
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary The abstract discusses the application of decision trees to explain support vector regression models. Decision trees are shown to outperform popular local explanation technique LIME in 87% of runs over five datasets, with a lower RMSE value when fitted to support vector regression. This improvement is statistically significant. Additionally, multi-linear regression also achieves better results than LIME in 73% of runs, although the difference is not statistically significant. When used as a local explanatory technique, decision trees outperform LIME. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary Decision trees are used to explain support vector regression models. The paper compares these trees to other methods like LIME and multi-linear regression. It finds that decision trees do a better job in most cases, especially when looking at the overall results (RMSE value). This is important because it helps us understand how machine learning models work. |
Keywords
* Artificial intelligence * Linear regression * Machine learning * Regression
