I think I've now implemented most of the optimizations mentioned in this topic.
I also adapted my code to use some of the neat things I found in Daniel's code. The implementation now supports most of the optimization parameters for decision trees, which I found on: http://scikit-learn.org/stable/modules/generated/sklearn.tree.DecisionTreeClassifier.html The updated code is on: https://github.com/ZelphirKaltstahl/racket-ml One test I find hard to write and that is the one for evaluate-algorithm, because of all the things one needs to foresee to write it. If there are no more obvious significant inefficiencies, I'll mark this topic as finished. -- You received this message because you are subscribed to the Google Groups "Racket Users" group. To unsubscribe from this group and stop receiving emails from it, send an email to racket-users+unsubscr...@googlegroups.com. For more options, visit https://groups.google.com/d/optout.
