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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.
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.
\*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.
List[Union[ bigframes.dataframe.DataFrame
, bigframes.series.Series
]]
