Hi Jacob,
Is there anything we can do to get better generalized decision rules?
For example, after one tree fitting, select top (N-1) features by
feature_importance, and then do the fitting again.
Can this be helpful?
Best,
Rex
On Sun, Aug 30, 2015 at 8:07 AM, Jacob Schreiber <jmschreibe...@gmail.com>
wrote:
> Tree pruning is currently not supported in sklearn.
>
> On Sun, Aug 30, 2015 at 6:44 AM, Rex X <dnsr...@gmail.com> wrote:
>
>> Tree pruning process is very important to get a better decision tree.
>>
>> One idea is to recursively remove the leaf node which cause least hurt to
>> the decision tree.
>>
>> Any idea how to do this for the following sample case?
>>
>>
>> import pandas as pd
>>> from sklearn.datasets import load_iris
>>> from sklearn import tree
>>> import sklearn
>>>
>>> iris = sklearn.datasets.load_iris()
>>> clf = tree.DecisionTreeClassifier(class_weight={0 : 0.30, 1: 0.3,
>>> 2:0.4}, max_features="auto")
>>> clf.fit(iris.data, iris.target)
>>>
>>
>>
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