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
Amit Thombre
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.