To maximize accuracy, n_estimators should ideally be as high as possible,
yet we would like to use a reasonable value to limit training and
prediction times. The new warm_start option is a nice way to incrementally
add more trees until you reach a satisfying accuracy.
Warm start in linear models is more general. You can also use it for
computing a regularization path (computing the solutions for many different
alphas or Cs) or for refitting an existing model with more or less data.
M.
On Wed, Jul 1, 2015 at 4:42 AM, Andreas Mueller <t3k...@gmail.com> wrote:
> 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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