sklearn的precision_score, recall_score, f1_score使用

sklearn的precision_score, recall_score, f1_score使用。

from sklearn.metrics import precision_score, recall_score, f1_score

y_true = [0, 1, 1, 0, 1, 0]
y_pred = [1, 1, 1, 0, 0, 1]

p = precision_score(y_true, y_pred, average='binary')
r = recall_score(y_true, y_pred, average='binary')
f1score = f1_score(y_true, y_pred, average='binary')

print(p)
print(r)
print(f1score)

0.5
0.666666666667
0.571428571429

支持ndarray格式的数据：

from sklearn.metrics import precision_score, recall_score, f1_score
import numpy as np

y_true = np.array([[0, 1, 1], 
                   [0, 1, 0]])
y_pred = np.array([[1, 1, 1], 
                   [0, 0, 1]])

y_true = np.reshape(y_true, [-1])
y_pred = np.reshape(y_pred, [-1])

p = precision_score(y_true, y_pred, average='binary')
r = recall_score(y_true, y_pred, average='binary')
f1score = f1_score(y_true, y_pred, average='binary')

print(p)
print(r)
print(f1score)

0.5
0.666666666667
0.571428571429