我就废话不多说啦，直接上代码吧！

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

补充拓展：回归模型指标:MSE 、 RMSE、 MAE、R2

sklearn调用

# 测试集标签预测

y_predict = lin_reg.predict(X_test)

# 衡量线性回归的MSE 、 RMSE、 MAE、r2

from math import sqrt

from sklearn.metrics import mean_absolute_error

from sklearn.metrics import mean_squared_error

from sklearn.metrics import r2_score

print("mean_absolute_error:", mean_absolute_error(y_test, y_predict))

print("mean_squared_error:", mean_squared_error(y_test, y_predict))

print("rmse:", sqrt(mean_squared_error(y_test, y_predict)))

print("r2 score:", r2_score(y_test, y_predict))

原生实现

# 测试集标签预测

y_predict = lin_reg.predict(X_test)

# 衡量线性回归的MSE 、 RMSE、 MAE

mse = np.sum((y_test - y_predict) ** 2) / len(y_test)

rmse = sqrt(mse)

mae = np.sum(np.absolute(y_test - y_predict)) / len(y_test)

r2 = 1-mse/ np.var(y_test)

print("mse:",mse," rmse:",rmse," mae:",mae," r2:",r2)

相关公式

MSE

RMSE

MAE

R2

以上这篇python之MSE、MAE、RMSE的使用就是小编分享给大家的全部内容了，希望能给大家一个参考，也希望大家多多支持python博客。