Hi All
I have been working for a while on sklearn, targeting different real life
machine learning problems. I have experienced there is a large range of
problems were the tree algorithms are not at par, because of the way the
splits are done, (gt and lt) on values. This is specifically applicable for
rare event problems with extremely skewed data sets, extremely large data
sets, a data sets with skewness on individual values.
Subgroup Discovery is a great option to bridge the gap here (like we have
Cortana implemented in Java. Python will be much faster without a doubt.).
Is there any plan to get this incorporated within sklearn ?
Thanks
Regards
Debanjan
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