sklearn 精确率、召回率

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

    >>> 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.        ])
    
      
      
      
      
      
      
      
      
      
      
      
      
    

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

    >>> 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.])