I'm modeling a regression problem. An initial attempt yields the following:
labels.mean(): 0.00018132978443886167 labels.std(): 0.013450786078937208 predictions.mean(): 0.0005549060297198594 predictions.std(): 0.00430255476385355
As you can see, the mean is off, and the standard deviation is totally different. I wonder what does it indicate?
My guess: does it mean that my features are not discriminative enough, so that the model see examples w/ positive and negative labels alike, hence the small variance in the output?
I'm running the regression using
XGBRegressor, with early-stopping. I have 1M training examples, 100K validation examples (for early-stopping), and another 100K for testing purpose (for which the mean and the standard deviation are shown above).
I also checked that the label distribution of the three sets are mostly basically the same.