sample_weight和min_samples_split在决策树中的相互作用

发布于 2025-01-30 20:56:00 字数 230 浏览 3 评论 0原文

在sklearn.ensemble.randomforestclassifier中，如果我们同时定义sample_weight和min_samples_split，样品权重是否影响min_samples_split。例如，如果min_sample_split = 20，并且样本中的数据点的重量为2，则10个数据点满足min_sample_split条件？

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花间憩 2025-02-06 20:56:00

不，请参见; min_samples_split不考虑样本权重。与min_samples_leaf及其加权cousin min_weight_fraction_leaf（ source ）。

您的示例建议一个简单的实验检查：

from sklearn.tree import DecisionTreeClassifier
import numpy as np

X = np.array([1, 2, 3]).reshape(-1, 1)
y = [0, 0, 1]

tree = DecisionTreeClassifier()
tree.fit(X, y)
print(len(tree.tree_.feature))  # number of nodes
# 3

tree.set_params(min_samples_split=10)
tree.fit(X, y)
print(len(tree.tree_.feature))
# 1

tree.set_params(min_samples_split=10)
tree.fit(X, y, sample_weight=[20, 20, 20])
print(len(tree.tree_.feature))
# 1; the sample weights don't count to make 
#    each sample "large" enough for min_samples_split

No, see the source; min_samples_split does not take into consideration sample weights. Compare to min_samples_leaf and its weighted cousin min_weight_fraction_leaf (source).

Your example suggests an easy experiment to check:

from sklearn.tree import DecisionTreeClassifier
import numpy as np

X = np.array([1, 2, 3]).reshape(-1, 1)
y = [0, 0, 1]

tree = DecisionTreeClassifier()
tree.fit(X, y)
print(len(tree.tree_.feature))  # number of nodes
# 3

tree.set_params(min_samples_split=10)
tree.fit(X, y)
print(len(tree.tree_.feature))
# 1

tree.set_params(min_samples_split=10)
tree.fit(X, y, sample_weight=[20, 20, 20])
print(len(tree.tree_.feature))
# 1; the sample weights don't count to make 
#    each sample "large" enough for min_samples_split

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