sklearn.metrics中的f1-score简介

sklearn.metrics.f1_score(y_true, y_pred, *, labels=None, pos_label=1, average='binary', sample_weight=None, zero_division='warn')

>>> from sklearn.metrics import precision_score, recall_score, f1_score
>>> y_true = [0, 1, 0, 0, 1, 0, 1]
>>> y_pred = [0, 1, 0, 0, 0, 1, 0]
# 计算二分类情况下的average = 'macro' 'micro' 'binary'
# 二分类情况下，也能用macro和micro，但一般用binary
>>> f1_score(y_true, y_pred,average='macro')
0.5333333333333332
>>> precision_score(y_true, y_pred, average='macro')
0.55
>>> recall_score(y_true, y_pred, average='macro')
0.5416666666666666
>>> precision_score(y_true, y_pred, average='micro')
0.5714285714285714
>>> recall_score(y_true, y_pred, average='micro')
0.5714285714285714
>>> f1_score(y_true, y_pred,average='micro')
0.5714285714285714
>>> f1_score(y_true, y_pred,average='binary')
0.4
>>> recall_score(y_true, y_pred, average='binary')
0.3333333333333333
>>> precision_score(y_true, y_pred, average='binary')
0.5
>>> f1_score(y_true, y_pred) # 二分类情况下，默认使用binary
0.4

>>> from sklearn.metrics import precision_score, recall_score, 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')
0.26666666666666666
>>> f1_score(y_true, y_pred, average='micro')
0.3333333333333333
>>> f1_score(y_true, y_pred, average=None) # 相当于binary，为每个类别计算binary的f1分数，但是不能输入binary，要输入None
array([0.8, 0. , 0. ])
>>> 0.8/3
0.26666666666666666