EstimatorReport.metrics.prediction_error#

EstimatorReport.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 display on the scatter plot. If None, no subsampling will be 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 train_test_split
>>> from skore import EstimatorReport
>>> X, y = load_diabetes(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> regressor = Ridge()
>>> report = EstimatorReport(regressor, **split_data)
>>> display = report.metrics.prediction_error()
>>> display.plot(perfect_model_kwargs={"color": "tab:red"})