I have a dataset
(X, y) where
X is multi-dimensional features and
y is the class label of each sample and it is a continues value between [-1,1]. I am using MLPRegressor as machine learning model to be used for prediction. To evaluate my model, I use several regression metrics found here, specifically
sklearn.mean_squared_error. After training the model, I got the following results:
Variance_score: 0.98 R^2_score: 0.98 mean_squared_error: 0.02
I understand that for variance and r2 scores, the best value would be 1.0. However, I don't know what would be the best value of mean_squared_error. What does
0.02 tell? Is the smaller the better or the higher the better? Does the value always set in a certain range?