I'm trying to fit a decision tree over some data which has ~40K rows and ~200 features. The response variable, y
, is ordinal and takes values {1,2,3}
or {1,2,3,4}
depending on the problem definition.
If I treat y
as a categorical/factor variable, the response time for the training CART (using the rpart
package) and testing (80/20 split) takes 20-30 seconds.
However, as soon as I try to use the rpartScore
package in R and frame y
as ordinal, the training doesn't complete even after close to 30-40 minutes.
I'm not very familiar with CART and the implementations here. I'm looking for hints to understand theoretically why this could be the case.
Resources for ordinal CARTs would be helpful. Especially an analysis of convergence-related issues.
rpart
, if it works? $\endgroup$ – Antoine Aug 19 '15 at 18:23