StratifiedKFold 和 KFold 的比较_stratifykfolf

• 将全部训练集S分成k个不相交的子集，假设S中的训练样例个数为m，那么每一个自己有m/k个训练样例，相应的子集为{s1，s2，...，sk}
• 
  每次从分好的子集里面，拿出一个作为测试集，其他k-1个作为训练集
• 
  在k-1个训练集上训练出学习器模型，把这个模型放到测试集上，得到分类率的平均值，作为该模型或者假设函数的真实分类率

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

Parameters

• n_splits : int, default=3

Number of folds. Must be at least 2.

• shuffle : boolean, optional

Whether to shuffle each stratification of the data before splitting into batches.

• random_state :

int, RandomState instance or None, optional, default=None

If int, random_state is the seed used by the random number generatorIf RandomState instance, random_state is the random number generator;

If None, the random number generator is the RandomState instance used

by `np.random`. Used when ``shuffle`` == True.

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])
 
sfolder=StratifiedKFold(n_splits=4,random_state=0,shuffle=False)
floder = KFold(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(" ") 

StratifiedKFold                                                                             KFold