Hi Jacob and Trevor,
Which part of the source code we can modify to add a new attribute to
DecisionTreeClassifier.tree_, to count the number of samples of each class
within each node?
Could you point me the right direction?
Best,
Rex
On Sun, Aug 30, 2015 at 8:12 AM, Jacob Schreiber <jmschreibe...@gmail.com>
wrote:
> This value is computed while building the tree, but is not kept in the
> tree.
>
> On Sun, Aug 30, 2015 at 7:02 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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