sklearn.metrics.f1_score

sklearn.metrics.f1_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)[source]

Parameters:

y_true : 数据真实标签 Ground truth (correct) target values.

y_pred : 分类器分类标签 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 : [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’] 多类/多标签目标需要此参数。如果没有，则返回每个类的分数。

‘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

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例子：

>>> from sklearn.metrics import f1_score
>>> y_true = [0, 1, 2, 0, 1, 2]
>>> y_pred = [0, 2, 1, 0, 0, 1]
>>> f1_score(y_true, y_pred, average='macro')  
0.26...
>>> f1_score(y_true, y_pred, average='micro')  
0.33...
>>> f1_score(y_true, y_pred, average='weighted')  
0.26...
>>> f1_score(y_true, y_pred, average=None)
array([ 0.8,  0. ,  0. ])