在机器学习和深度学习中，将分类任务的预测结果分为以下四种，被称作混淆矩阵：
True Positive(TP)：预测出的为正例，标签值也为正例，预测正确
False Negative(FN)：预测出的为负例，标签值为正例，预测错误
False Positive(FP)：预测出的为正例，标签值为负例，预测错误
True Negative(TN)：预测出的为负例，标签值为负例，预测正确
precision
查准率、精确率=正确预测到的正例数/预测正例总数
precision = TP/(TP+FP)
recall
查全率、召回率=正确预测到的正例数/实际正例总数
recall = TP/(TP+FN)
F1 score
F1 score 为精确率与召回率的调和均值
2/F1 = 1/P+1/R
F1 score = 2TP/(2TP+FP+FN)
准确率accuracy和精确率precision都高的情况下，F1 score也会显得很高
点击查看代码
import os
import sys
from pymetric import AllMetrics
def example_1():
"""for regression task
"""
A = [[0.1, 0.2, 0.2, 0.6], [0.5, 0.1, 0.2, 0.3]]
B = [[0.7, 0.5, 0.8, 0.7], [0.1, 0.3, 0.7, 0.1]]
print ('Y_truth', A)
print ('Y_pred ', B)
for method in ['abs_error', 'rmse', 'r2', 'ndcg', 'cos']:
try:
print (method, AllMetrics.measure(A, B, method))
except Exception as e:
print ('Exception: %s' % repr(e))
def example_2():
"""for classification task
"""
A = [[1, 0, 0, 1, 0, 1], [1, 0, 0, 1, 0, 1]]
B = [[0, 1, 1, 0, 1, 0], [0.2, 0.3, 0.7, 0.1, 0.3, 0.8]]
print ('Y_truth', A)
print ('Y_pred ', B)
for method in ['precision', 'recall', 'f1_score', 'auc', 'map']:
try:
print (method, AllMetrics.measure(A, B, method))
except Exception as e:
print ('Exception: %s' % repr(e))
if __name__ == '__main__':
#example_1()
example_2()