`sklearn.metrics`.plot_precision_recall_curve¶

sklearn.metrics.plot_precision_recall_curve(estimator, X, y, *, sample_weight=None, response_method='auto', name=None, ax=None, pos_label=None, **kwargs)[source]¶

DEPRECATED: Function plot_precision_recall_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the class methods: PrecisionRecallDisplay.from_predictions or PrecisionRecallDisplay.from_estimator.

Plot Precision Recall Curve for binary classifiers.

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

Read more in the User Guide.

Deprecated since version 1.0: plot_precision_recall_curve is deprecated in 1.0 and will be removed in 1.2. Use one of the following class methods: from_predictions or from_estimator.

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,): Binary target values.
sample_weightarray-like of shape (n_samples,), default=None: Sample weights.
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, the name of the estimator is used.
axmatplotlib axes, default=None: Axes object to plot on. If None, a new figure and axes is created.
pos_labelstr or int, 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.

New in version 0.24.
**kwargsdict: Keyword arguments to be passed to matplotlib’s plot.

Returns:

displayPrecisionRecallDisplay: Object that stores computed values.

sklearn.metrics.plot_precision_recall_curve¶

`sklearn.metrics`.plot_precision_recall_curve¶