I am trying to fit a model of the form
where $x$ is a vector variable and $u$ an independent random noise. I tried to fit the unknown function $f$ by different machine learning methods (SVM, MARS, etc.)
I split my data into training and validation sets. After fitting the model on the training set, I test it on a validation set, and I plot $y$-realized versus $y$-predicted, and do a linear regression on that scatter plot. I obtain a very small intercept but a coefficient of $0.5$, while I was naively expecting a coefficient near to 1.
Does this regression coefficient tell me something about the quality of fit of my original model or about the original model itself?