Python sklearn GroupKFold用法及代碼示例

本文簡要介紹python語言中 sklearn.model_selection.GroupKFold 的用法。

用法:

class sklearn.model_selection.GroupKFold(n_splits=5)

K-fold 具有非重疊組的迭代器變體。

同一組不會出現在兩個不同的折疊中(不同組的數量必須至少等於折疊的數量)。

在每個折疊中不同組的數量大致相同的意義上，折疊大致平衡。

在用戶指南中閱讀更多信息。

參數：

n_splits：整數，默認=5

折疊次數。必須至少為 2。

例子：

>>> import numpy as np
>>> from sklearn.model_selection import GroupKFold
>>> X = np.array([[1, 2], [3, 4], [5, 6], [7, 8]])
>>> y = np.array([1, 2, 3, 4])
>>> groups = np.array([0, 0, 2, 2])
>>> group_kfold = GroupKFold(n_splits=2)
>>> group_kfold.get_n_splits(X, y, groups)
2
>>> print(group_kfold)
GroupKFold(n_splits=2)
>>> for train_index, test_index in group_kfold.split(X, y, groups):
...     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]
...     print(X_train, X_test, y_train, y_test)
...
TRAIN: [0 1] TEST: [2 3]
[[1 2]
 [3 4]] [[5 6]
 [7 8]] [1 2] [3 4]
TRAIN: [2 3] TEST: [0 1]
[[5 6]
 [7 8]] [[1 2]
 [3 4]] [3 4] [1 2]