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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