PrecisionRecallCurveDisplay

class skore.PrecisionRecallCurveDisplay(precision, recall, *, average_precision, estimator_name, pos_label=None, data_source=None)

Precision Recall visualization.

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

Parameters:

precisiondict of list of ndarray

Precision values. The structure is:

• for binary classification:

  • the key is the positive label.

  • the value is a list of ndarray, each ndarray being the precision.

• for multiclass classification:

  • the key is the class of interest in an OvR fashion.

  • the value is a list of ndarray, each ndarray being the precision.

recalldict of list of ndarray

Recall values. The structure is:

• for binary classification:

  • the key is the positive label.

  • the value is a list of ndarray, each ndarray being the recall.

• for multiclass classification:

  • the key is the class of interest in an OvR fashion.

  • the value is a list of ndarray, each ndarray being the recall.

average_precisiondict of list of float

Average precision. The structure is:

• for binary classification:

  • the key is the positive label.

  • the value is a list of float, each float being the average precision.

• for multiclass classification:

  • the key is the class of interest in an OvR fashion.

  • the value is a list of float, each float being the average precision.

estimator_namestr

Name of the estimator.

pos_labelint, float, bool or str, default=None

The class considered as the positive class. If None, the class will not be shown in the legend.

data_source{“train”, “test”, “X_y”}, default=None

The data source used to compute the precision recall curve.

Attributes:

ax_matplotlib Axes

Axes with precision recall curve.

figure_matplotlib Figure

Figure containing the curve.

lines_list of matplotlib Artist

Precision recall curve.

chance_levels_matplotlib Artist or None

The chance level line. It is None if the chance level is not plotted.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_breast_cancer(return_X_y=True), random_state=0
... )
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.precision_recall()
>>> display.plot(pr_curve_kwargs={"color": "tab:red"})

help()

Display available attributes and methods using rich.

plot(ax=None, *, estimator_name=None, pr_curve_kwargs=None, despine=True)

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, default=None

Name of the estimator used to plot the precision-recall curve. If None, we use the inferred name from the estimator.

pr_curve_kwargsdict or list of dict, default=None

Keyword arguments to be passed to matplotlib’s plot for rendering the precision-recall curve(s).

despinebool, default=True

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

Notes

The average precision (cf. average_precision_score()) in scikit-learn is computed without any interpolation. To be consistent with this metric, the precision-recall curve is plotted without any interpolation as well (step-wise style).

You can change this style by passing the keyword argument drawstyle="default". However, the curve will not be strictly consistent with the reported average precision.

Examples

>>> from sklearn.datasets import load_breast_cancer
>>> from sklearn.linear_model import LogisticRegression
>>> from sklearn.model_selection import train_test_split
>>> from skore import EstimatorReport
>>> X_train, X_test, y_train, y_test = train_test_split(
...     *load_breast_cancer(return_X_y=True), random_state=0
... )
>>> classifier = LogisticRegression(max_iter=10_000)
>>> report = EstimatorReport(
...     classifier,
...     X_train=X_train,
...     y_train=y_train,
...     X_test=X_test,
...     y_test=y_test,
... )
>>> display = report.metrics.precision_recall()
>>> display.plot(pr_curve_kwargs={"color": "tab:red"})