决策树中 sample_weight 和 min_samples_split 之间的交互

Question

在sklearn.ensemble.RandomForestClassifier中，如果我们同时定义sample_weight和min_samples_split，样本权重是否会影响min_samples_split。比如min_sample_split = 20，样本中数据点的权重都是2，那么有10个数据点满足min_sample_split条件？

Answer 1

否，参见 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)。

你的例子建议一个简单的实验来检查：

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

决策树中 sample_weight 和 min_samples_split 之间的交互

Interaction between sample_weight and min_samples_split in decision tree

python

scikit-learn

random-forest

decision-tree