PredictionErrorDisplay#

class skore.PredictionErrorDisplay(*, y_true, y_pred, estimator_name, data_source=None)[source]#

Visualization of the prediction error of a regression model.

This tool can display “residuals vs predicted” or “actual vs predicted” using scatter plots to qualitatively assess the behavior of a regressor, preferably on held-out data points.

An instance of this class is should created by EstimatorReport.metrics.prediction_error(). You should not create an instance of this class directly.

Parameters:

y_truelist of ndarray of shape (n_samples,): True values.
y_predlist of ndarray of shape (n_samples,): Prediction values.
estimator_namestr: Name of the estimator.
data_source{“train”, “test”, “X_y”}, default=None: The data source used to display the prediction error.

Attributes:

line_matplotlib Artist: Optimal line representing y_true == y_pred. Therefore, it is a diagonal line for kind="predictions" and a horizontal line for kind="residuals".
errors_lines_matplotlib Artist or None: Residual lines. If with_errors=False, then it is set to None.
scatter_matplotlib Artist: Scatter data points.
ax_matplotlib Axes: Axes with the different matplotlib axis.
figure_matplotlib Figure: Figure containing the scatter and lines.

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
... )
>>> classifier = Ridge()
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.prediction_error()
>>> display.plot(kind="actual_vs_predicted")

help()[source]#: Display available attributes and methods using rich.

plot(ax=None, *, estimator_name=None, kind='residual_vs_predicted', scatter_kwargs=None, line_kwargs=None, despine=True)[source]#

Plot visualization.

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

Parameters:

axmatplotlib axes, default=None

Axes object to plot on. If None, a new figure and axes is created.

estimator_namestr

Name of the estimator used to plot the prediction error. If None, we used the inferred name from the estimator.

kind{“actual_vs_predicted”, “residual_vs_predicted”}, default=”residual_vs_predicted”

The type of plot to draw:

“actual_vs_predicted” draws the observed values (y-axis) vs. the predicted values (x-axis).
“residual_vs_predicted” draws the residuals, i.e. difference between observed and predicted values, (y-axis) vs. the predicted values (x-axis).

scatter_kwargsdict, default=None

Dictionary with keywords passed to the matplotlib.pyplot.scatter call.

line_kwargsdict, default=None

Dictionary with keyword passed to the matplotlib.pyplot.plot call to draw the optimal line.

despinebool, default=True

Whether to remove the top and right spines from the plot.

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
... )
>>> classifier = Ridge()
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.prediction_error()
>>> display.plot(kind="actual_vs_predicted")

PredictionErrorDisplay#

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