8.17.1.4. sklearn.metrics.precision_score¶
- 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.
Parameters : y_true : array, shape = [n_samples]
True targets
y_pred : array, shape = [n_samples]
Predicted targets
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.
- macro:
Average over classes (does not take imbalance into account).
- micro:
Average over instances (takes imbalance into account). This implies that precision == recall == f1
- weighted:
Average weighted by support (takes imbalance into account). Can result in f1 score that is not between precision and recall.
Returns : precision : float
Precision of the positive class in binary classification or weighted average of the precision of each class for the multiclass task