sklearn.metrics.f1_score用法
F1 measure
sklearn.metrics.precision_score(y_true,
y_pred,
labels=None,
pos_label=1,
average='binary',
sample_weight=None,
zero_division='warn'
)
Parameters:
- y_true :1d array-like, or label indicator array / sparse matrix
- y_pred :1d array-like, or label indicator array / sparse matrix
- average : 计算类型 string, [None, ‘binary’ (default), ‘micro’, ‘macro’, ‘samples’, ‘weighted’] average参数定义了该指标的计算方法，二分类时average参数默认是binary，多分类时，可选参数有micro、macro、weighted和samples。
- sample_weight : 样本权重
参数average
| 选项 | 含义 |
|---|---|
| binary | 二分类 |
| micro | 统计全局TP和FP来计算 |
| macro | 计算每个标签的未加权均值（不考虑不平衡） |
| weighted | 计算每个标签等等加权均值（考虑不平衡） |
| samples | 计算每个实例找出其均值 |
| None | 返回每类的精确度 |
Returns:
- precision:float (if average is not None) or array of float, shape = [n_unique_labels]
>>> 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. ])
>>> y_true = [0, 0, 0, 0, 0, 0]
>>> y_pred = [0, 0, 0, 0, 0, 0]
>>> f1_score(y_true, y_pred, zero_division=1)
1.0...