[sklearn] KFold与StratifiedKFold用法_stratifiedkfold遇到一类只有一个样本

1. 概览

KFold和StratifiedKFold的作用都是用于配合交叉验证的需求，将数据分割成训练集和测试集。

2. 区别

KFold随机分割数据，不会考虑数据的分布情况。
StratifiedKFold会根据原始数据的分布情况，分割出同分布的数据。

3. 实验

3.1 代码

from sklearn.model_selection import KFold
from sklearn.model_selection import StratifiedKFold
import numpy as np

X = np.array([
    [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],
    [81,82,83,84]
])

y = np.array([1,1,0,0,0,1,1,0])

KFold = KFold(n_splits=2, shuffle=True)
sKFold = StratifiedKFold(n_splits=2, shuffle=True)

print("===KFold split===")
for train, test in KFold.split(X, y):
    print('train index:\n',train)
    print('train X value:\n',X[train])
    print('train y value:\n',y[train])
    print('test i
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