skrub.DataOp.skb.mark_as_y

DataOp.skb.mark_as_y()

Mark this DataOp as being the y table.

This is used for cross-validation and hyperparameter selection: operations done before skb.mark_as_X() and skb.mark_as_y() are executed on the entire data and cannot benefit from hyperparameter tuning. Returns a copy; the original DataOp is left unchanged.

Returns:

The input DataOp, which has been marked as being y

See also

skrub.y()

skrub.y(value) can be used as a shorthand for skrub.var('y', value).skb.mark_as_y().

Notes

During cross-validation, all the previous steps are first executed, until X and y have been materialized. Then, those are split into training and testing sets. The following steps in the DataOp plan are fitted on the train data, and applied to test data, within each split.

This means that any step that comes before mark_as_X() or mark_as_y(), meaning that it is needed to compute X and y, sees the full dataset and cannot benefit from hyperparameter tuning. So we should be careful to start our learner by building X and y, and to use mark_as_X() and mark_as_y() as soon as possible.

Note: this marks the DataOp in-place and also returns it.

Examples

>>> import skrub
>>> orders = skrub.var('orders', skrub.datasets.toy_orders(split='all').orders)
>>> X = orders.drop(columns='delayed', errors='ignore').skb.mark_as_X()
>>> delayed = orders['delayed']
>>> delayed.skb.is_y
False
>>> y = delayed.skb.mark_as_y()
>>> y.skb.is_y
True

Note the original is left unchanged

>>> delayed.skb.is_y
False

Now if we run cross-validation:

>>> from sklearn.dummy import DummyClassifier
>>> pred = X.skb.apply(DummyClassifier(), y=y)
>>> pred.skb.cross_validate(cv=2)['test_score']
0    0.666667
1    0.666667
Name: test_score, dtype: float64

First (outside of the cross-validation loop) X and y are computed. Then, they are split into training and test sets. Then the rest of the learner (in this case the last step, the DummyClassifier) is evaluated on those splits.

Please see the examples gallery for more information.