Home - News ( United States | United Kingdom | Italy | Germany ) - Football scores

Showing content from https://cloud.google.com/python/docs/reference/bigframes/2.1.0/bigframes.ml.model_selection.KFold below:

Class KFold (2.1.0) | Python client library

Stay organized with collections Save and categorize content based on your preferences.

• 2.15.0 (latest)
• 2.14.0
• 2.13.0
• 2.12.0
• 2.11.0
• 2.10.0
• 2.9.0
• 2.8.0
• 2.7.0
• 2.6.0
• 2.5.0
• 2.4.0
• 2.3.0
• 2.2.0
• 2.1.0
• 2.0.0
• 1.42.0
• 1.41.0
• 1.40.0
• 1.39.0
• 1.38.0
• 1.37.0
• 1.36.0
• 1.35.0
• 1.34.0
• 1.33.0
• 1.32.0
• 1.31.0
• 1.30.0
• 1.29.0
• 1.28.0
• 1.27.0
• 1.26.0
• 1.25.0
• 1.24.0
• 1.22.0
• 1.21.0
• 1.20.0
• 1.19.0
• 1.18.0
• 1.17.0
• 1.16.0
• 1.15.0
• 1.14.0
• 1.13.0
• 1.12.0
• 1.11.1
• 1.10.0
• 1.9.0
• 1.8.0
• 1.7.0
• 1.6.0
• 1.5.0
• 1.4.0
• 1.3.0
• 1.2.0
• 1.1.0
• 1.0.0
• 0.26.0
• 0.25.0
• 0.24.0
• 0.23.0
• 0.22.0
• 0.21.0
• 0.20.1
• 0.19.2
• 0.18.0
• 0.17.0
• 0.16.0
• 0.15.0
• 0.14.1
• 0.13.0
• 0.12.0
• 0.11.0
• 0.10.0
• 0.9.0
• 0.8.0
• 0.7.0
• 0.6.0
• 0.5.0
• 0.4.0
• 0.3.0
• 0.2.0

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.

[[["Easy to understand","easyToUnderstand","thumb-up"],["Solved my problem","solvedMyProblem","thumb-up"],["Other","otherUp","thumb-up"]],[["Hard to understand","hardToUnderstand","thumb-down"],["Incorrect information or sample code","incorrectInformationOrSampleCode","thumb-down"],["Missing the information/samples I need","missingTheInformationSamplesINeed","thumb-down"],["Other","otherDown","thumb-down"]],["Last updated 2025-08-12 UTC."],[],[]]

RetroSearch is an open source project built by @garambo | Open a GitHub Issue

Search and Browse the WWW like it's 1997 | Search results from DuckDuckGo

HTML: 3.2 | Encoding: UTF-8 | Version: 0.7.4