Hi Ian,
2013/7/7 Ian Ozsvald <i...@ianozsvald.com>
> Hi all. I'm following the RandomForest code (in dev from a 1 week old
> checkout). As I understand it (and similar to the previous post - I
> have some RF usage experience but nothing fundamental), RF uses a
> weighted sample of examples to learn *and* a random subset of features
> when building its decision trees.
>
correct - although weighted samples are optional - usually, RF takes a
bootstrap sample and this is implemented via sample_weights (e.g. a sample
that is picked two times for the bootstrap has weight 2.0)
>
> Does the scikit-learn implementation use a random subset of features?
> I've followed the code in forest.py and I can't find where the choice
> might be made. I haven't looked at the C code for the DecisionTree.
>
Its in the implementation of DecisionTree - see sklearn/tree/_tree.pyx -
look for the for loop over ``features``.
>
> I'm interested to learn the lower bound of the number of random
> features that can be chosen.
>
could you elaborate on that?
>
> I'm also curious to understand where we can restrict the depth of the
> RandomForest classifier. All I can see is that in forest.py the
> constructor takes but ignores the max_depth argument:
> class RandomForestClassifier(ForestClassifier):
> ...
> def __init__(self,
> n_estimators=10,
> criterion="gini",
> max_depth=None,
> ...
> super(RandomForestClassifier, self).__init__(
> base_estimator=DecisionTreeClassifier(),
> ...
>
> base.py._make_estimator just clones the existing base_estimator. Am I
> missing something?
>
after cloning it calls ``set_params`` with ``estimator_params`` -
``'max_depth'`` is one of those.
best,
Peter
>
> Thanks for listening,
> Ian.
>
> --
> Ian Ozsvald (A.I. researcher)
> i...@ianozsvald.com
>
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Peter Prettenhofer
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