python之MSE、MAE、RMSE
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
