I saw the new parameter warm_start on the RandomForestClassifier class and
was curious about what is its most common use. I can see two uses for it:
(1) instead of fitting a huge forest in one go, fit it many times in the
same data set and check the improvement in cross validation, (2) fit the
data in a whole new data set and so doing some kind of "online mini-batch
Random Forest", which might be useful for some of my use cases.
The first use is very straightforward and it seems obvious that a forest
trained two times in the same dataset with warm_start=True will have
similar performance with a forest trained only "once" with double
n_estimators.
The second case though seem to break a few assumptions of the statistical
ideas behind Random Forests. Have anyone tried it successfully?
Thanks,
Rafael Calsaverini
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