model_selection.

from sklearn.model_selection import
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用于数据集划分

评估评估

RepeatedKFold

重复K折交叉验证，一般10次10折交叉验证

ref

sklearn-api

使用sklearn进行交叉验证

参数：

n_splits: 折叠次数，至少为2。int, default=5

n_repeats: 重复交叉验证次数。int，default=10

random_state: 控制随机性，类似随机种子。int, RandomState 或者 None，default=None

方法：

get_n_splits(X = None, y = None, group = None)

返回交叉验证器中的拆分迭代的数量

split(X,y = None, group = None)

生成索引（将数据拆分为训练集和测试集）

具体使用方式见demo

demo

>>> import numpy as np
>>> from sklearn.model_selection import RepeatedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rkf = RepeatedKFold(n_splits=2, n_repeats=2, random_state=2652124)
>>> 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]
...
TRAIN: [0 1] TEST: [2 3]
TRAIN: [2 3] TEST: [0 1]
TRAIN: [1 2] TEST: [0 3]
TRAIN: [0 3] TEST: [1 2]
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