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PrecisionRecallDisplay

classsklearn.metrics.PrecisionRecallDisplay(precision, recall, *, average_precision=None, estimator_name=None, pos_label=None, prevalence_pos_label=None)[source]

Precision Recall visualization.

It is recommend to use from_estimator or from_predictions to create a PrecisionRecallDisplay. All parameters are stored as attributes.

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" in plot, from_estimator, or from_predictions. However, the curve will not be strictly consistent with the reported average precision.

Examples

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import (precision_recall_curve,
...                              PrecisionRecallDisplay)
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.svm import SVC
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(X, y,
...                                                     random_state=0)
>>> clf = SVC(random_state=0)
>>> clf.fit(X_train, y_train)
SVC(random_state=0)
>>> predictions = clf.predict(X_test)
>>> precision, recall, _ = precision_recall_curve(y_test, predictions)
>>> disp = PrecisionRecallDisplay(precision=precision, recall=recall)
>>> disp.plot()
<...>
>>> plt.show()

../../_images/sklearn-metrics-PrecisionRecallDisplay-1.png

classmethodfrom_estimator(estimator, X, y, *, sample_weight=None, pos_label=None, drop_intermediate=False, response_method='auto', name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]

Plot precision-recall curve given an estimator and some data.

Parameters:

estimatorestimator instance: Fitted classifier or a fitted Pipeline in which the last estimator is a classifier.
X{array-like, sparse matrix} of shape (n_samples, n_features): Input values.
yarray-like of shape (n_samples,): Target values.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.
pos_labelint, float, bool or str, default=None: The class considered as the positive class when computing the precision and recall metrics. By default, estimators.classes_[1] is considered as the positive class.
drop_intermediatebool, default=False: Whether to drop some suboptimal thresholds which would not appear on a plotted precision-recall curve. This is useful in order to create lighter precision-recall curves.

Added in version 1.3.
response_method{‘predict_proba’, ‘decision_function’, ‘auto’}, default=’auto’: Specifies whether to use predict_proba or decision_function as the target response. If set to ‘auto’, predict_proba is tried first and if it does not exist decision_function is tried next.
namestr, default=None: Name for labeling curve. If None, no name is used.
axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
plot_chance_levelbool, default=False: Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during from_estimator or from_predictions call.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayPrecisionRecallDisplay

Notes

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

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import PrecisionRecallDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...         X, y, random_state=0)
>>> clf = LogisticRegression()
>>> clf.fit(X_train, y_train)
LogisticRegression()
>>> PrecisionRecallDisplay.from_estimator(
...    clf, X_test, y_test)
<...>
>>> plt.show()

../../_images/sklearn-metrics-PrecisionRecallDisplay-2.png

classmethodfrom_predictions(y_true, y_pred, *, sample_weight=None, pos_label=None, drop_intermediate=False, name=None, ax=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[source]

Plot precision-recall curve given binary class predictions.

Parameters:

y_truearray-like of shape (n_samples,): True binary labels.
y_predarray-like of shape (n_samples,): Estimated probabilities or output of decision function.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.
pos_labelint, float, bool or str, default=None: The class considered as the positive class when computing the precision and recall metrics.
drop_intermediatebool, default=False: Whether to drop some suboptimal thresholds which would not appear on a plotted precision-recall curve. This is useful in order to create lighter precision-recall curves.

Added in version 1.3.
namestr, default=None: Name for labeling curve. If None, name will be set to "Classifier".
axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
plot_chance_levelbool, default=False: Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during from_estimator or from_predictions call.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayPrecisionRecallDisplay

Notes

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

>>> import matplotlib.pyplot as plt
>>> from sklearn.datasets import make_classification
>>> from sklearn.metrics import PrecisionRecallDisplay
>>> from sklearn.model_selection import train_test_split
>>> from sklearn.linear_model import LogisticRegression
>>> X, y = make_classification(random_state=0)
>>> X_train, X_test, y_train, y_test = train_test_split(
...         X, y, random_state=0)
>>> clf = LogisticRegression()
>>> clf.fit(X_train, y_train)
LogisticRegression()
>>> y_pred = clf.predict_proba(X_test)[:, 1]
>>> PrecisionRecallDisplay.from_predictions(
...    y_test, y_pred)
<...>
>>> plt.show()

../../_images/sklearn-metrics-PrecisionRecallDisplay-3.png

plot(ax=None, *, name=None, plot_chance_level=False, chance_level_kw=None, despine=False, **kwargs)[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.
namestr, default=None: Name of precision recall curve for labeling. If None, use estimator_name if not None, otherwise no labeling is shown.
plot_chance_levelbool, default=False: Whether to plot the chance level. The chance level is the prevalence of the positive label computed from the data passed during from_estimator or from_predictions call.

Added in version 1.3.
chance_level_kwdict, default=None: Keyword arguments to be passed to matplotlib’s plot for rendering the chance level line.

Added in version 1.3.
despinebool, default=False: Whether to remove the top and right spines from the plot.

Added in version 1.6.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayPrecisionRecallDisplay: Object that stores computed values.

Notes

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.

Gallery examples

Visualizations with Display Objects

Precision-Recall

Post-tuning the decision threshold for cost-sensitive learning

Precision-Recall

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Licensed under the 3-clause BSD License.
https://scikit-learn.org/1.6/modules/generated/sklearn.metrics.PrecisionRecallDisplay.html