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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