python机器学习-乳腺癌细胞挖掘（博主亲自录制视频）
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原文链接
https://blog.csdn.net/wqh_jingsong/article/details/77896449
StratifiedKFold用法类似Kfold，但是他是分层采样，确保训练集，测试集中各类别样本的比例与原始数据集中相同。
例子：
import numpy as np
from sklearn.model_selection import KFold,StratifiedKFold
X=np.array([
[1,2,3,4],
[11,12,13,14],
[21,22,23,24],
[31,32,33,34],
[41,42,43,44],
[51,52,53,54],
[61,62,63,64],
[71,72,73,74]
]) y=np.array([1,1,0,0,1,1,0,0])
#n_folds这个参数没有，引入的包不同，
floder = KFold(n_splits=4,random_state=0,shuffle=False)
sfolder = StratifiedKFold(n_splits=4,random_state=0,shuffle=False) for train, test in sfolder.split(X,y):
print('Train: %s | test: %s' % (train, test))
print(" ") for train, test in floder.split(X,y):
print('Train: %s | test: %s' % (train, test))
print(" ")
结果：
1.
Train: [1 3 4 5 6 7] | test: [0 2]
Train: [0 2 4 5 6 7] | test: [1 3]
Train: [0 1 2 3 5 7] | test: [4 6]
Train: [0 1 2 3 4 6] | test: [5 7]
2.
Train: [2 3 4 5 6 7] | test: [0 1]
Train: [0 1 4 5 6 7] | test: [2 3]
Train: [0 1 2 3 6 7] | test: [4 5]
Train: [0 1 2 3 4 5] | test: [6 7]
分析：可以看到StratifiedKFold 分层采样交叉切分，确保训练集，测试集中各类别样本的比例与原始数据集中相同。
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