我就廢話不多說啦，直接上程式碼吧！

target = [1.5,2.1,3.3,-4.7,-2.3,0.75]
prediction = [0.5,1.5,-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))#誤差絕對值
 
 
print("Square Error: ",squaredError)
print("Absolute Value of Error: ",absError)
 
 
print("MSE = ",sum(squaredError) / len(squaredError))#均方誤差MSE
 
 
from math import sqrt
print("RMSE = ",sqrt(sum(squaredError) / len(squaredError)))#均方根誤差RMSE
print("MAE = ",sum(absError) / len(absError))#平均絕對誤差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)))#標準差

補充拓展：迴歸模型指標:MSE 、 RMSE、 MAE、R2

sklearn呼叫

# 測試集標籤預測
y_predict = lin_reg.predict(X_test)

# 衡量線性迴歸的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))

原生實現

# 測試集標籤預測
y_predict = lin_reg.predict(X_test)
# 衡量線性迴歸的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)

相關公式

MSE

RMSE

MAE

R2

以上這篇python之MSE、MAE、RMSE的使用就是小編分享給大家的全部內容了，希望能給大家一個參考，也希望大家多多支援我們。