Module model_selection (1.20.0)

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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.

Classes

KFold

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.

Parameters
Name	Description
n_splits	int Number of folds. Must be at least 2. Default to 5.
random_state	Optional[int] 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.

Modules Functions

train_test_split

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.

Parameters
Name	Description
\*arrays	bigframes.dataframe.DataFrame or bigframes.series.Series A sequence of BigQuery DataFrames or Series that can be joined on their indexes.
test_size	default None 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.
train_size	default None The proportion of the dataset to include in the train split. If None, this will default to the complement of test_size.
random_state	default None A seed to use for randomly choosing the rows of the split. If not set, a random split will be generated each time.

Returns
Type	Description
List[Union[bigframes.dataframe.DataFrame, bigframes.series.Series]]	A list of BigQuery DataFrames or Series.