机器学习中MSE、MAE、RMSE的python实现

target = [1.5, 2.1, 3.3, -4.7, -2.3, 0.75]
prediction = [0.5, 1.5, 2.1, -2.2, 0.1, -0.5]
 
 
error = []
for i in range(len(target)):
    error.append(target[i] - prediction[i])
 
 
print("Errors: ", error)
print(error)
 
 
 
 
 
 
squaredError = []
absError = []
for val in error:
    squaredError.append(val * val)#target-prediction之差平方 
    absError.append(abs(val))#误差绝对值
 
 
print("Square Error: ", squaredError)
print("Absolute Value of Error: ", absError)
 
 
 
 
print("MSE = ", sum(squaredError) / len(squaredError))#均方误差MSE
 
 
 
 
from math import sqrt
print("RMSE = ", sqrt(sum(squaredError) / len(squaredError)))#均方根误差RMSE
print("MAE = ", sum(absError) / len(absError))#平均绝对误差MAE
 
 
targetDeviation = []
targetMean = sum(target) / len(target)#target平均值
for val in target:
    targetDeviation.append((val - targetMean) * (val - targetMean))
print("Target Variance = ", sum(targetDeviation) / len(targetDeviation))#方差
 
 
print("Target Standard Deviation = ", sqrt(sum(targetDeviation) / len(targetDeviation)))#标准差

转载：https://blog.csdn.net/llx1026/article/details/77752121