sklearn.metrics
.precision_score¶
- sklearn.metrics.precision_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')[source]¶
Compute the precision.
The precision is the ratio
tp / (tp + fp)
wheretp
is the number of true positives andfp
the number of false positives. The precision is intuitively the ability of the classifier not to label as positive a sample that is negative.The best value is 1 and the worst value is 0.
Read more in the User Guide.
- Parameters:
- y_true1d array-like, or label indicator array / sparse matrix
Ground truth (correct) target values.
- y_pred1d array-like, or label indicator array / sparse matrix
Estimated targets as returned by a classifier.
- labelsarray-like, default=None
The set of labels to include when
average != 'binary'
, and their order ifaverage is None
. Labels present in the data can be excluded, for example to calculate a multiclass average ignoring a majority negative class, while labels not present in the data will result in 0 components in a macro average. For multilabel targets, labels are column indices. By default, all labels iny_true
andy_pred
are used in sorted order.Changed in version 0.17: Parameter
labels
improved for multiclass problem.- pos_labelint, float, bool or str, default=1
The class to report if
average='binary'
and the data is binary. If the data are multiclass or multilabel, this will be ignored; settinglabels=[pos_label]
andaverage != 'binary'
will report scores for that label only.- average{‘micro’, ‘macro’, ‘samples’, ‘weighted’, ‘binary’} or None, default=’binary’
This parameter is required for multiclass/multilabel targets. If
None
, the scores for each class are returned. Otherwise, this determines the type of averaging performed on the data:'binary'
:Only report results for the class specified by
pos_label
. This is applicable only if targets (y_{true,pred}
) are binary.'micro'
:Calculate metrics globally by counting the total true positives, false negatives and false positives.
'macro'
:Calculate metrics for each label, and find their unweighted mean. This does not take label imbalance into account.
'weighted'
:Calculate metrics for each label, and find their average weighted by support (the number of true instances for each label). This alters ‘macro’ to account for label imbalance; it can result in an F-score that is not between precision and recall.
'samples'
:Calculate metrics for each instance, and find their average (only meaningful for multilabel classification where this differs from
accuracy_score
).
- sample_weightarray-like of shape (n_samples,), default=None
Sample weights.
- zero_division{“warn”, 0.0, 1.0, np.nan}, default=”warn”
Sets the value to return when there is a zero division.
Notes: - If set to “warn”, this acts like 0, but a warning is also raised. - If set to
np.nan
, such values will be excluded from the average.New in version 1.3:
np.nan
option was added.
- Returns:
- precisionfloat (if average is not None) or array of float of shape (n_unique_labels,)
Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task.
See also
precision_recall_fscore_support
Compute precision, recall, F-measure and support for each class.
recall_score
Compute the ratio
tp / (tp + fn)
wheretp
is the number of true positives andfn
the number of false negatives.PrecisionRecallDisplay.from_estimator
Plot precision-recall curve given an estimator and some data.
PrecisionRecallDisplay.from_predictions
Plot precision-recall curve given binary class predictions.
multilabel_confusion_matrix
Compute a confusion matrix for each class or sample.
Notes
When
true positive + false positive == 0
, precision returns 0 and raisesUndefinedMetricWarning
. This behavior can be modified withzero_division
.Examples
>>> import numpy as np >>> from sklearn.metrics import precision_score >>> y_true = [0, 1, 2, 0, 1, 2] >>> y_pred = [0, 2, 1, 0, 0, 1] >>> precision_score(y_true, y_pred, average='macro') 0.22... >>> precision_score(y_true, y_pred, average='micro') 0.33... >>> precision_score(y_true, y_pred, average='weighted') 0.22... >>> precision_score(y_true, y_pred, average=None) array([0.66..., 0. , 0. ]) >>> y_pred = [0, 0, 0, 0, 0, 0] >>> precision_score(y_true, y_pred, average=None) array([0.33..., 0. , 0. ]) >>> precision_score(y_true, y_pred, average=None, zero_division=1) array([0.33..., 1. , 1. ]) >>> precision_score(y_true, y_pred, average=None, zero_division=np.nan) array([0.33..., nan, nan])
>>> # multilabel classification >>> y_true = [[0, 0, 0], [1, 1, 1], [0, 1, 1]] >>> y_pred = [[0, 0, 0], [1, 1, 1], [1, 1, 0]] >>> precision_score(y_true, y_pred, average=None) array([0.5, 1. , 1. ])
Examples using sklearn.metrics.precision_score
¶
Probability Calibration curves