성능평가지표, 모델 평가 방법 Python Code

성능 평가 지표 Python code(파이썬 코드)

 

 * 함수를 정의해서 직접 구하는 방식

# MAE

def MAE(y_true, y_pred): 
	return np.mean(np.abs((y_true - y_pred)))

print("MAE == ", MAE(y_true, y_pred))

# MAPE

def MAPE(y_true, y_pred): 
	return np.mean(np.abs((y_true - y_pred) / y_true))

print("MAPE == ", MAPE(y_true, y_pred))



# MSE

def MSE(y_true, y_pred):
	return np.mean(np.square((y_true - y_pred)))
    
print("MSE == ", MSE(y_true, y_pred))

# RMSE
print("RMSE == ", np.sqrt(MSE(y_true, y_pred)))

* sklearn.metrics 사용하는 방식

# MAE
from sklearn.metrics import mean_absolute_error
mean_absolute_error(y_true, y_pred)



# MSE
from sklearn.metrics import mean_squared_error
mean_squared_error(y_true, y_pred)



# RMSE
np.sqrt(MSE(y_true, y_pred))