- sklearn.metrics.precision_score(y_true, y_pred, labels=None, pos_label=1, average='weighted')¶
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.
y_true : array, shape = [n_samples]
Ground truth (correct) target values.
y_pred : array, shape = [n_samples]
Estimated targets as returned by a classifier.
labels : array
Integer array of labels.
pos_label : int
In the binary classification case, give the label of the positive class (default is 1). Everything else but pos_label is considered to belong to the negative class. Set to None in the case of multiclass classification.
average : string, [None, ‘micro’, ‘macro’, ‘weighted’ (default)]
In the multiclass classification case, this determines the type of averaging performed on the data.
Do not perform any averaging, return the scores for each class.
Average over classes (does not take imbalance into account).
Average over instances (takes imbalance into account). This implies that precision == recall == F1.
Average weighted by support (takes imbalance into account). Can result in F-score that is not between precision and recall.
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.
In the binary case:
>>> from sklearn.metrics import precision_score >>> y_pred = [0, 1, 0, 0] >>> y_true = [0, 1, 0, 1] >>> precision_score(y_true, y_pred) 1.0
In the multiclass case:
>>> 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. ])