Trevor, those attributes are present while the tree is being built, but are
not kept in the final tree object.
On Sun, Aug 30, 2015 at 7:15 AM, Trevor Stephens <trev.steph...@gmail.com>
wrote:
> n_node_samples is the count of actual dataset samples in each
> node. weighted_n_node_samples is the same, weighted by the class_weight
> and/or sample_weight.
>
> On Sun, Aug 30, 2015 at 8:02 AM, Rex X <dnsr...@gmail.com> wrote:
>
>> DecisionTreeClassifier.tree_.n_node_samples is the total number of
>> samples in all classes of one node, and
>> DecisionTreeClassifier.tree_.value is the computed weight for each class
>> of one node. Only if the sample_weight and class_weight of this
>> DecisionTreeClassifier
>> is one, then this attribute equals the number of samples of each class of
>> one node.
>>
>> But for the general case with a given sample_weight and class_weight, is
>> there any attribute telling us the number of samples of each class
>> within one node?
>>
>>
>> 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)
>>
>>
>> # the total number of samples in all classes of each node
>> clf.tree_.n_node_samples
>>
>> # the computed weight for each class of each node
>> clf.tree_.value
>>
>>
>>
>>
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