PredictionErrorDisplay#

class skore.PredictionErrorDisplay(*, prediction_error, range_y_true, range_y_pred, range_residuals, data_source, ml_task, report_type)[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 should be created by EstimatorReport.metrics.prediction_error(). You should not create an instance of this class directly.

Parameters:

prediction_errorDataFrame

The prediction error data to display. The columns are

estimator_name
split (may be null)
y_true
y_pred
residuals.

range_y_trueRangeData

Global range of the true values.

range_y_predRangeData

Global range of the predicted values.

range_residualsRangeData

Global range of the residuals.

data_source{“train”, “test”, “X_y”}

The data source used to display the prediction error.

ml_task{“regression”, “multioutput-regression”}

The machine learning task.

report_type{“comparison-cross-validation”, “comparison-estimator”, “cross-validation”, “estimator”}

The type of report.

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_list of 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 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)
>>> classifier = Ridge()
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.prediction_error()
>>> display.plot(kind="actual_vs_predicted")

frame()[source]#

Get the data used to create the prediction error plot.

Returns:

DataFrame

A DataFrame containing the prediction error data with columns depending on the report type:

estimator_name: Name of the estimator (when comparing estimators)
split: Cross-validation split ID (when doing cross-validation)
y_true: True target values
y_pred: Predicted target values
residuals: Difference between true and predicted values (y_true - y_pred)

Examples

>>> from sklearn.datasets import load_diabetes
>>> from sklearn.linear_model import Ridge
>>> from skore import train_test_split, EstimatorReport
>>> X, y = load_diabetes(return_X_y=True)
>>> split_data = train_test_split(X=X, y=y, random_state=0, as_dict=True)
>>> reg = Ridge()
>>> report = EstimatorReport(reg, **split_data)
>>> display = report.metrics.prediction_error()
>>> df = display.frame()

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

plot(*, estimator_name=None, kind='residual_vs_predicted', data_points_kwargs=None, perfect_model_kwargs=None, despine=True)[source]#

Plot visualization.

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

Parameters:

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).

data_points_kwargsdict, default=None

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

perfect_model_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 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)
>>> classifier = Ridge()
>>> report = EstimatorReport(classifier, **split_data)
>>> display = report.metrics.prediction_error()
>>> display.plot(kind="actual_vs_predicted")

set_style(**kwargs)[source]#

Set the style parameters for the display.

Parameters:

**kwargsdict: Style parameters to set. Each parameter name should correspond to a a style attribute passed to the plot method of the display.

Returns:

selfobject: Returns the instance itself.

Raises:

ValueError: If a style parameter is unknown.

static style_plot(plot_func)[source]#

Apply consistent style to skore displays.

This decorator: 1. Applies default style settings 2. Executes plot_func

Parameters:

plot_funccallable: The plot function to be decorated.

Returns:

callable: The decorated plot function.

PredictionErrorDisplay#

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