Why getting very high values for MSE/MAE/MAPE when R2 score is very good I am applying different regression models (RF, Knn, etc) on some well-known datasets (bike sharing, diabetics, etc). The value of R2 is very good. From the R2 score, we can say that the model is working well (though this is not true for every case). So, I have MSE, MAE, and MAPE methods. But, the value of MAE/MAPE/MSE is very high which means that the prediction of the models is very bad and very far from the actual values (true labels).
The accuracy scores of the datasets
Name        MAE      MAPE      R2     MSE  
Bike        24.56    0.34      0.95   1615
Diabetics   0.06     2321.20   0.87   0.03  

The formula used to calculate MAPE
MAPE = np.mean(np.abs(predictions - y_test) / (y_test + 1e-5))

I would like to know, when the value of R2 value is good (very high), at the same time how it could possible that the prediction from the model is very bad (that we can get from the MSE/MAPE/MAE scores)
The description of datasets
Name     Count Mean    Std     Min    Max
Bike     17379 189.46  181.38  1.00   977
Diabetics 768   0.34    0.47    0      1


The quick answer is that $R^2$ measures a reduction in variance, compared to always guessing $\bar y$, no matter the predictors. What your results tell me is that the variance from always guessing $\bar y$ is so gigantic that even a huge $R^2$ value like $0.9$ or $0.99$ still does not let you get as accurate as you want or need for your application. Maybe you need $R^2>0.9999$ for your task (ten-thousand-fold reduction in variance).
