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Showing content from https://cloud.google.com/python/docs/reference/bigframes/1.19.0/bigframes.ml.model_selection below:

Module model_selection (1.19.0) | Python client library

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Functions for test/train split and model tuning. This module is styled after scikit-learn's model_selection module: https://scikit-learn.org/stable/modules/classes.html#module-sklearn.model_selection.

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.

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.

train_test_split(
    *arrays: typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series],
    test_size: typing.Optional[float] = None,
    train_size: typing.Optional[float] = None,
    random_state: typing.Optional[int] = None,
    stratify: typing.Optional[bigframes.series.Series] = None
) -> typing.List[typing.Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]

Splits dataframes or series into random train and test subsets.

A sequence of BigQuery DataFrames or Series that can be joined on their indexes.

The proportion of the dataset to include in the test split. If None, this will default to the complement of train_size. If both are none, it will be set to 0.25.

The proportion of the dataset to include in the train split. If None, this will default to the complement of test_size.

A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time.

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Last updated 2025-08-12 UTC.

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