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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.
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. |
Methods
get_n_splits
get_n_splits() -> int
Returns the number of splitting iterations in the cross-validator.
Returns | |
---|---|
Type | Description |
int |
the number of splitting iterations in the cross-validator. |
split
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.
Parameters | |
---|---|
Name | Description |
X |
bigframes.dataframe.DataFrame or bigframes.series.Series
BigFrames DataFrame or Series of shape (n_samples, n_features) Training data, where |
y |
bigframes.dataframe.DataFrame, bigframes.series.Series or None :Yields: *X_train (bigframes.dataframe.DataFrame or bigframes.series.Series)* -- The training data for that split. X_test (bigframes.dataframe.DataFrame or bigframes.series.Series): The testing data for that split. y_train (bigframes.dataframe.DataFrame, bigframes.series.Series or None): The training label for that split. y_test (bigframes.dataframe.DataFrame, bigframes.series.Series or None): The testing label for that split.
BigFrames DataFrame, Series of shape (n_samples,) or None. The target variable for supervised learning problems. Default to None. |