CrossValidationReport.metrics.prediction_error#

CrossValidationReport.metrics.prediction_error(*, data_source='test', X=None, y=None, subsample=1000, seed=None)[source]#

Plot the prediction error of a regression model.

Extra keyword arguments will be passed to matplotlib’s plot.

Parameters:
data_source{“test”, “train”, “X_y”}, default=”test”

The data source to use.

  • “test” : use the test set provided when creating the report.

  • “train” : use the train set provided when creating the report.

  • “X_y” : use the provided X and y to compute the metric.

Xarray-like of shape (n_samples, n_features), default=None

New data on which to compute the metric. By default, we use the validation set provided when creating the report.

yarray-like of shape (n_samples,), default=None

New target on which to compute the metric. By default, we use the target provided when creating the report.

subsamplefloat, int or None, default=1_000

Sampling the samples to be shown on the scatter plot. If float, it should be between 0 and 1 and represents the proportion of the original dataset. If int, it represents the number of samples applied. by default, 1,000 samples or less will be displayed.

seedint, default=None

The seed used to initialize the random number generator used for the subsampling.

Returns:
PredictionErrorDisplay

The prediction error display.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from skore import CrossValidationReport
>>> X, y = load_diabetes(return_X_y=True)
>>> regressor = Ridge()
>>> report = CrossValidationReport(regressor, X=X, y=y, cv_splitter=2)
>>> display = report.metrics.prediction_error()
>>> display.plot(
...     kind="actual_vs_predicted", perfect_model_kwargs={"color": "tab:red"}
... )