RepeatedStratifiedKFold#

class sklearn.model_selection.RepeatedStratifiedKFold(*, n_splits=5, n_repeats=10, random_state=None)[source]#

重复分层 K-Fold 交叉验证器。

Repeats Stratified K-Fold n times with different randomization in each repetition.

Read more in the User Guide.

注意

基于类标签的分层解决的是工程问题而非统计问题。有关详细信息，请参阅基于类标签分层的交叉验证迭代器。

参数:

n_splitsint, default=5: 折叠数。必须至少为 2。
n_repeatsint, default=10: Number of times cross-validator needs to be repeated.
random_stateint, RandomState instance or None, default=None: Controls the generation of the random states for each repetition. Pass an int for reproducible output across multiple function calls. See Glossary.

另请参阅

RepeatedKFold: Repeats K-Fold n times.

注意事项

随机 CV 拆分器在每次调用 split 时可能会返回不同的结果。您可以通过将 random_state 设置为整数来使结果相同。

示例

>>> import numpy as np
>>> from sklearn.model_selection import RepeatedStratifiedKFold
>>> X = np.array([[1, 2], [3, 4], [1, 2], [3, 4]])
>>> y = np.array([0, 0, 1, 1])
>>> rskf = RepeatedStratifiedKFold(n_splits=2, n_repeats=2,
...     random_state=36851234)
>>> rskf.get_n_splits()
4
>>> print(rskf)
RepeatedStratifiedKFold(n_repeats=2, n_splits=2, random_state=36851234)
>>> for i, (train_index, test_index) in enumerate(rskf.split(X, y)):
...     print(f"Fold {i}:")
...     print(f"  Train: index={train_index}")
...     print(f"  Test:  index={test_index}")
...
Fold 0:
  Train: index=[1 2]
  Test:  index=[0 3]
Fold 1:
  Train: index=[0 3]
  Test:  index=[1 2]
Fold 2:
  Train: index=[1 3]
  Test:  index=[0 2]
Fold 3:
  Train: index=[0 2]
  Test:  index=[1 3]

get_metadata_routing()[source]#

获取此对象的元数据路由。

请查阅用户指南，了解路由机制如何工作。

返回:

routingMetadataRequest: 封装路由信息的 MetadataRequest。

get_n_splits(X=None, y=None, groups=None)[source]#

返回在实例化交叉验证器时使用 n_splits 参数设置的拆分迭代次数。

参数:

Xarray-like of shape (n_samples, n_features), default=None: 始终忽略，存在是为了保持 API 兼容性。
yshape 为 (n_samples,), default=None 的 array-like: 始终忽略，存在是为了保持 API 兼容性。
groups形状为 (n_samples,) 的类数组对象，默认=None: 始终忽略，存在是为了保持 API 兼容性。

返回: