ML之回归预测：回归预测问题中常用的误差度量方法——MSE、RMSE、MAE

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ML之回归预测：回归预测问题中常用的误差度量方法——MSE、RMSE、MAE

输出结果MSE、RMSE、MAE

回归预测问题中常用的误差度量方法的实现代码

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 the errors
print("Errors ",)
print(error) 

#calculate the squared errors and absolute value of errors
squaredError = []
absError = []
for val in error:
    squaredError.append(val*val)
    absError.append(abs(val))
    
#print squared errors and absolute value of errors
print("Squared Error")
print(squaredError)  
print("Absolute Value of Error")
print(absError)     
#calculate and print mean squared error MSE
print("MSE = ", sum(squaredError)/len(squaredError)) 


from math import sqrt
#calculate and print square root of MSE (RMSE)
print("RMSE = ", sqrt(sum(squaredError)/len(squaredError)))  
#calculate and print mean absolute error MAE
print("MAE = ", sum(absError)/len(absError))  


#compare MSE to target variance
targetDeviation = []
targetMean = sum(target)/len(target)
for val in target:
    targetDeviation.append((val - targetMean)*(val - targetMean))

#print the target variance
print("Target Variance = ", sum(targetDeviation)/len(targetDeviation)) 
#print the the target standard deviation (square root of variance)
print("Target Standard Deviation = ", sqrt(sum(targetDeviation)/len(targetDeviation)))