K-Folds cross-validator-K折交叉验证实现

简单K折交叉验证源码：（

将数据集拆分为k个连续的折叠（默认情况下不进行混洗）。

然后将每个折叠用作一次验证，而剩下的k-1个折叠形成训练集。

）

import numpy as np
from sklearn.model_selection import KFold
X = np.array([[, ], [, ], [, ], [, ]])
y = np.array([, , , ])
kf = KFold(n_splits=)
kf.get_n_splits(X)

print(kf)

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]

# 输出

TRAIN: [2 3] TEST: [0 1]
TRAIN: [0 1] TEST: [2 3]

参考教程：https://scikit-learn.org/stable/modules/generated/sklearn.model_selection.KFold.html     And    https://machinelearningmastery.com/k-fold-cross-validation/

N次K折交叉验证源码：（重复K折n次，每次重复具有不同的随机性）

import numpy as np
from sklearn.model_selection import RepeatedKFold
X = np.array([[, ], [, ], [, ], [, ]])
y = np.array([, , , ])
rkf = RepeatedKFold(n_splits=, n_repeats=, random_state=)     #2次2折交叉验证
for train_index, test_index in rkf.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]   
# 输出：

TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]