  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.
