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, **kwargs)[source]¶

Plot Precision Recall Curve for binary classifiers.

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

Read more in the User Guide.

Parameters

estimator : estimator instance

Trained classifier.

X : {array-like, sparse matrix} of shape (n_samples, n_features)

Input values.

y : array-like of shape (n_samples,)

Binary target values.

sample_weight : array-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.

name : str, default=None

Name for labeling curve. If None, the name of the estimator is used.

ax : matplotlib axes, default=None

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

**kwargs : dict

Keyword arguments to be passed to matplotlib’s plot.

Returns

display : PrecisionRecallDisplay

Object that stores computed values.

Examples using sklearn.metrics.plot_precision_recall_curve¶

Precision-Recall¶