EstimatorReport.metrics.rmse#

EstimatorReport.metrics.rmse(*, data_source='test', X=None, y=None, multioutput='raw_values')[source]#

Compute the root mean squared error.

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.

multioutput{“raw_values”, “uniform_average”} or array-like of shape (n_outputs,), default=”raw_values”

Defines aggregating of multiple output values. Array-like value defines weights used to average errors. The other possible values are:

  • “raw_values”: Returns a full set of errors in case of multioutput input.

  • “uniform_average”: Errors of all outputs are averaged with uniform weight.

By default, no averaging is done.

Returns:
float or list of n_outputs

The root mean squared error.

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_diabetes(return_X_y=True), random_state=0
... )
>>> regressor = Ridge()
>>> report = EstimatorReport(
...     regressor,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> report.metrics.rmse()
56.5...