sklearn.metrics.precision_score()

sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average='binary', sample_weight=None) [source]

Compute the precision

The precision is the ratio tp / (tp + fp) where tp is the number of true positives and fp 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_true : 1d array-like, or label indicator array / sparse matrix Ground truth (correct) target values. y_pred : 1d array-like, or label indicator array / sparse matrix Estimated targets as returned by a classifier. labels : list, optional The set of labels to include when average != 'binary', and their order if average 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 in y_true and y_pred are used in sorted order. Changed in version 0.17: parameter labels improved for multiclass problem. pos_label : str or int, 1 by default The class to report if average='binary' and the data is binary. If the data are multiclass or multilabel, this will be ignored; setting labels=[pos_label] and average != 'binary' will report scores for that label only. average : string, [None, ?binary? (default), ?micro?, ?macro?, ?samples?, ?weighted?] 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_weight : array-like of shape = [n_samples], optional Sample weights.

Returns:

precision : float (if average is not None) or array of float, 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.

Examples

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>>> 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.        ])

Examples using sklearn.metrics.precision_score

Probability Calibration curves