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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
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.
int
split
split
(
X
:
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
series
.
Series
],
y
:
typing
.
Optional
[
typing
.
Union
[
bigframes
.
dataframe
.
DataFrame
,
bigframes
.
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.
X
bigframes.dataframe.DataFrame
or bigframes.series.Series
BigFrames DataFrame or Series of shape (n_samples, n_features) Training data, where n_samples
is the number of samples and n_features
is the number of features.
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.
