If you're running a random forest with default parameters (max_depth=None,
min_samples_split=2,
min_samples_leaf=1, min_weight_fraction_leaf=0, max_leaf_nodes=None), the
size of the tree will tend towards the size of the dataset. Change some of
these parameters to reduce overfitting and model size.
On 10 April 2016 at 10:13, Piotr Płoński <pplonsk...@gmail.com> wrote:
> Hi All,
>
> I am saving RandomForestClassifier model from sklearn library with code
> below
>
> with open('/tmp/rf.model', 'wb') as f: cPickle.dump(RF_model, f)
>
> It takes a lot of space on my hard drive. There are only 50 trees in the
> model, however it takes over 50 MB on disk (analyzed dataset is ~ 20MB,
> with 21 features). Does anybody have idea why? I observe similar behavior
> for ExtraTreesClassifier.
>
> Best,
>
> Piotr
>
>
>
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