In [18]:

    

x1 = np.random.randint(1,6,1000)
x2 = np.random.randint(1,6,1000)


    

In [21]:

    

from sklearn.metrics import mean_squared_error, r2_score, mean_absolute_error
mse = mean_squared_error(x1, x2)
mae = mean_absolute_error(x1, x2)
r2 = r2_score(x1, x2)
print("mse : ", mse ,
      "\nmae : ", mae,
      "\n r2 : ", r2)


    

    




mse :  4.065 
mae :  1.613 
 r2 :  -1.01339788754

In [46]:

    

mse_list = []
mae_list = []
r2_list = []

for _ in range(5):
    x1 = np.random.randint(1,6,1000)
    x2 = np.random.randint(1,6,1000)
    
    mse = mean_squared_error(x1, x2)
    mae = mean_absolute_error(x1, x2)
    r2 = r2_score(x1, x2)
    
    mse_list.append(round(mse, 4))
    mae_list.append(round(mae, 4))
    r2_list.append(round(r2, 4))

print("mse_mean : ", np.mean(mse_list))
print("mse_list : ", mse_list)
print('---------------------')
print("mae_mean : ", np.mean(mae_list))
print("mae_list : ", mae_list)
print('---------------------')
print("r2_mean  : ", np.mean(r2_list))
print("r2_list  : ", r2_list)


    

    




mse_mean :  3.868
mse_list :  [4.235, 3.846, 3.839, 3.809, 3.611]
---------------------
mae_mean :  1.558
mae_list :  [1.641, 1.562, 1.551, 1.537, 1.499]
---------------------
r2_mean  :  -0.96284
r2_list  :  [-1.1596, -0.9293, -0.9179, -0.98, -0.8274]

In [ ]: