利用sklearn 计算 precision、recall、F1 score

精确度：precision，正确预测为正的，占全部预测为正的比例，TP / (TP+FP)
召回率：recall，正确预测为正的，占全部实际为正的比例，TP / (TP+FN)
F1-score：精确率和召回率的调和平均数，2 * precision*recall / (precision+recall)

from sklearn.metrics import confusion_matrix
from sklearn.metrics import classification_report
from sklearn.metrics import precision_recall_fscore_support
from sklearn.metrics import accuracy_score

actual = [0,0,0,0,1,1,0,3,3]
predicted = [0,0,0,0,1,1,2,3,3]

# 计算总的精度
acc = accuracy_score(actual, predicted)

# 计算混淆矩阵
confusion_matrix(actual, predicted)

结果：

由混淆矩阵计算每一类的precision、recall、F1-score:
例 class0: precision = 4/4+0+0+0 = 1; recall = 4/4+0+1+0 = 0.8; f1-score = 2 *1 *0.8/(1+0.8) = 0.89

# 计算precision, recall, F1-score, support
class_names = ['agree', 'disagree', 'discuss', 'unrelated']
print(classification_report(actual, predicted, target_names=class_names))

结果：

# 另一种=方式计算precision, recall, F1-score, support
pre, rec, f1, sup = precision_recall_fscore_support(actual, predicted)
print("precision:", pre, "\nrecall:", rec, "\nf1-score:", f1, "\nsupport:", sup)

结果：

可以看出两种计算方式结果相同。