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Class KFold (1.39.0) | Python client library

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KFold(n_splits: int = 5, *, random_state: typing.Optional[int] = None)

K-Fold cross-validator.

Split data in train/test sets. Split dataset into k consecutive folds.

Each fold is then used once as a validation while the k - 1 remaining folds form the training set.

Examples:

>>> import bigframes.pandas as bpd
>>> from bigframes.ml.model_selection import KFold
>>> bpd.options.display.progress_bar = None
>>> X = bpd.DataFrame({"feat0": [1, 3, 5], "feat1": [2, 4, 6]})
>>> y = bpd.DataFrame({"label": [1, 2, 3]})
>>> kf = KFold(n_splits=3, random_state=42)
>>> for i, (X_train, X_test, y_train, y_test) in enumerate(kf.split(X, y)):
...     print(f"Fold {i}:")
...     print(f"  X_train: {X_train}")
...     print(f"  X_test: {X_test}")
...     print(f"  y_train: {y_train}")
...     print(f"  y_test: {y_test}")
...
Fold 0:
  X_train:    feat0  feat1
1      3      4
2      5      6
<BLANKLINE>
[2 rows x 2 columns]
  X_test:    feat0  feat1
0      1      2
<BLANKLINE>
[1 rows x 2 columns]
  y_train:    label
1      2
2      3
<BLANKLINE>
[2 rows x 1 columns]
  y_test:    label
0      1
<BLANKLINE>
[1 rows x 1 columns]
Fold 1:
  X_train:    feat0  feat1
0      1      2
2      5      6
<BLANKLINE>
[2 rows x 2 columns]
  X_test:    feat0  feat1
1      3      4
<BLANKLINE>
[1 rows x 2 columns]
  y_train:    label
0      1
2      3
<BLANKLINE>
[2 rows x 1 columns]
  y_test:    label
1      2
<BLANKLINE>
[1 rows x 1 columns]
Fold 2:
  X_train:    feat0  feat1
0      1      2
1      3      4
<BLANKLINE>
[2 rows x 2 columns]
  X_test:    feat0  feat1
2      5      6
<BLANKLINE>
[1 rows x 2 columns]
  y_train:    label
0      1
1      2
<BLANKLINE>
[2 rows x 1 columns]
  y_test:    label
2      3
<BLANKLINE>
[1 rows x 1 columns]

Number of folds. Must be at least 2. Default to 5.

A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time. Default to None.

Returns the number of splitting iterations in the cross-validator.

split(
    X: typing.Union[
        bigframes.dataframe.DataFrame,
        bigframes.series.Series,
        pandas.core.frame.DataFrame,
        pandas.core.series.Series,
    ],
    y: typing.Optional[
        typing.Union[
            bigframes.dataframe.DataFrame,
            bigframes.series.Series,
            pandas.core.frame.DataFrame,
            pandas.core.series.Series,
        ]
    ] = None,
) -> typing.Generator[
    tuple[
        typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series, NoneType],
        ...,
    ],
    None,
    None,
]

Generate indices to split data into training and test set.

Except as otherwise noted, the content of this page is licensed under the Creative Commons Attribution 4.0 License, and code samples are licensed under the Apache 2.0 License. For details, see the Google Developers Site Policies. Java is a registered trademark of Oracle and/or its affiliates.

Last updated 2025-08-12 UTC.

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