sklearn 精确率、召回率

精确率

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

• Examples

>>> 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.        ])

召回率

sklearn.metrics.recall_score(y_true, y_pred, labels=None, pos_label=1, average=’binary’, sample_weight=None)

• Examples

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