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
orfrom_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
- display
PrecisionRecallDisplay
Object that stores computed values.
- display
See also
precision_recall_curve
Compute precision-recall pairs for different probability thresholds.
PrecisionRecallDisplay
Precision Recall visualization.