KFold/StratifiedKFold/GroupKFold

• 1. sklearn.model_selection.KFold
• • 1.1 KFold().split(x) 循环获取分割数据
  • 1.2 cross_validate(cv=KFold()) 作为cv参数
• 2. sklearn.model_selection.StratifiedKFold
• 3. sklearn.model_selection.GroupKFold

1. sklearn.model_selection.KFold

1.1 KFold().split(x) 循环获取分割数据

from sklearn.model_selection import KFoldX = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9] # 索引与值一样
'''
不管样本的标签(y)分布
shuffle 每次分割前打乱顺序
random_state shuffle=True时使用，设定后重复运行数据分组不变
'''
kf = KFold(n_splits=5, shuffle=False)
for train, test in kf.split(X, y):print(train, test)
'''
[2 3 4 5 6 7 8 9] [0 1]
[0 1 4 5 6 7 8 9] [2 3]
[0 1 2 3 6 7 8 9] [4 5]
[0 1 2 3 4 5 8 9] [6 7]
[0 1 2 3 4 5 6 7] [8 9]
'''

kf = KFold(n_splits=5, shuffle=True)
for train, test in kf.split(X, y):print(train, test)
'''
[0 1 2 4 5 6 7 9] [3 8]
[1 2 3 4 5 7 8 9] [0 6]
[0 1 3 4 6 7 8 9] [2 5]
[0 1 2 3 5 6 8 9] [4 7]
[0 2 3 4 5 6 7 8] [1 9]
'''

1.2 cross_validate(cv=KFold()) 作为cv参数

• 【sklearn】sklearn.model_selection.cross_val_score/cross_validate

2. sklearn.model_selection.StratifiedKFold

• 作用： 划分后的训练集和测试集数据分布与原数据相同
  即：原始标签中类别占比=训练标签中类别占比=验证标签中类别占比
• 【sklearn】模型融合_堆叠法 StackingClassfier\Regressor参数cv

from sklearn.model_selection import StratifiedKFoldX = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]skf = StratifiedKFold(n_splits=5, shuffle=False)
for train, test in skf.split(X, y):print(train, test)
'''
[1 2 3 5 6 7 8 9] [0 4]
[0 2 3 4 6 7 8 9] [1 5]
[0 1 3 4 5 7 8 9] [2 6]
[0 1 2 4 5 6 8 9] [3 7]
[0 1 2 3 4 5 6 7] [8 9]
'''

skf = StratifiedKFold(n_splits=5, shuffle=True)
for train, test in skf.split(X, y):print(train, test)
'''
[0 1 2 4 5 6 7 8] [3 9]
[0 1 3 4 6 7 8 9] [2 5]
[1 2 3 4 5 6 8 9] [0 7]
[0 2 3 4 5 6 7 9] [1 8]
[0 1 2 3 5 7 8 9] [4 6]
'''

3. sklearn.model_selection.GroupKFold

• 只有n_splits一个参数
• 作用： 保证同一个group的样本不会同时出现在训练集和测试集上
  即：一个group的多个样本要么出现在训练集，要么都出现在测试集
• 意义： 若一个group中的样本即用于训练也用于测试，模型能充分学习该group样本的特征并在测试集表现良好，但遇到新group会表现较差。

from sklearn.model_selection import GroupKFoldX = [0, 1, 2, 3, 4, 5, 6, 7, 8, 9]
y = [0, 0, 0, 0, 1, 1, 1, 1, 1, 1]
groups = [1, 1, 1, 2, 3, 3, 4, 4, 5, 5]gkf = GroupKFold(n_splits=5)
for train, test in gkf.split(X, y, groups=groups):print(train, test)
'''
[3 4 5 6 7 8 9] [0 1 2]
[0 1 2 3 4 5 6 7] [8 9]
[0 1 2 3 4 5 8 9] [6 7]
[0 1 2 3 6 7 8 9] [4 5]
[0 1 2 4 5 6 7 8 9] [3]
'''