[gwerbin]

That's a common phenomenon in model fitting, depending on the type of model. In both old school regression and neural networks, the fitted model does not distinguish between specific training examples and other inputs. So specific input-output pairs from the training data don't get special privilege. In fact it's often a good thing that models don't just memorize inputt-output pairs from training, because that allows them to smooth over uncaptured sources of variation such as people all being slightly different as well as measurement error.

In this case they had to customize the model fitting to try to get the error closer to zero specifically on those attributes.

[sorenjan]

Yes, but why are they estimating the features when they are already available? They can estimate the other measurements from height etc, and just use the known inputs as is. I don't get the point of passing them through a model at all.

[arkadiuss]

The previous response was exactly right. The estimated features are impacting height, so the height can't be set then do the rest. It also cannot be tuned afterwards because it would change the mass. So vicious circle.