机器学习笔记：sklearn交叉验证之KFold与StratifiedKFold

一、交叉验证

机器学习中常用交叉验证函数：KFold 和 StratifiedKFold。

方法导入：

from sklearn.model_selection import KFold, StratifiedKFold

• StratifiedKFold：采用分层划分的方法（分层随机抽样思想），验证集中不同类别占比与原始样本的比例一致，划分时需传入标签特征
• KFold：默认随机划分训练集、验证集

二、KFold交叉验证

1.使用语法

sklearn.model_selection.KFold(n_splits=3, # 最少2折
                             shuffle=False, # 是否打乱
                             random_state=None)

2.实操

• get_n_splits -- 返回折数
• split -- 切分

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

X = np.array([[1,2], [3,4], [5,6], [7,8]])
y = np.array([1,2,3,4])
kf = KFold(n_splits=2)
kf.get_n_splits() # 2
print(kf) # KFold(n_splits=2, random_state=None, shuffle=False)

# 此处的split只需传入数据，不需要传入标签
for train_index, test_index in kf.split(X):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]
'''
TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]
'''

三、StratifiedKFold交叉验证

1.使用语法

sklearn.model_selection.StratifiedKFold(n_splits=3, # 同KFold参数
                                       shuffle=False,
                                       random_state=None)

2.实操

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

X = np.array([[1,2], [3,4], [5,6], [7,8]])
y = np.array([1,0,0,1])
skf = StratifiedKFold(n_splits=2)
skf.get_n_splits() # 2
print(skf) # StratifiedKFold(n_splits=2, random_state=None, shuffle=False)

# 同时传入数据集和标签
for train_index, test_index in skf.split(X, y):
    print("TRAIN:", train_index, "TEST:", test_index)
    X_train, X_test = X[train_index], X[test_index]
    y_train, y_test = y[train_index], y[test_index]

注意：拆分的折数必须大于等于标签类别，否则报错：

ValueError: n_splits=2 cannot be greater than the number of members in each class.

参考链接：sklearn.model_selection.KFold

参考链接：sklearn.model_selection.StratifiedKFold

参考链接：python sklearn中KFold与StratifiedKFold