It does the second.
You need to feed it the same data.
On 06/25/2015 11:59 AM, Rafael Calsaverini wrote:
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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