There is no overtraining here, it isn't deep learning and there appears no parameterisation. If parameterisation was occurring on the training set that can and does cause over-tightening.

Essentially, you haven't included a data split for parameterisation, i.e. there is no parameterisation. The theory of a parameterisation is it must be distinct from training (and obviously) testing. It allows parameter tuned without overtightening. Lasso regression will require parameterisation.

I don't know about the survival regressions for machine learning, but lasso/ridge regression are standard approaches.

There is no overtraining here, it isn't deep learning and there appears no parameterisation. If parameterisation was occurring on the training set that can and does cause over-tightening.

Essentially, you haven't included a data split for parameterisation, i.e. there is no parameterisation. The theory of a parameterisation is it must be distinct from training (and obviously) testing. It allows parameter tuned without overtightening. Lasso regression will require parameterisation.

I don't know about the survival regressions for machine learning, but lasso/ridge regression are standard approaches.

So just to explain, what you do is have a split for parameters. Thus instead of 80:20 split it could be 70:20:10 split where the 10% is used for parameter optimisation. Bad code risks leakage from the parameter split and that can result in over-tightening.

There is no overtraining here, it isn't deep learning and there appears no parameterisation. If parameterisation was occurring on the training set that can and does cause over-tightening.

Essentially, you haven't included a data split for parameterisation, i.e. there is no parameterisation. The theory of a parameterisation is it must be distinct from training (and obviously) testing. It allows parameter tuned without overtightening. I'm lasso regression will require parameterisation.

I don't know about the survival regressions for machine learning, but lasso/ridge regression are standard approaches.

There is no overtraining here, it isn't deep learning and there appears no parameterisation. If parameterisation was occurring on the training set that can and does cause over-tightening.

Essentially, you haven't included a data split for parameterisation, i.e. there is no parameterisation. The theory of a parameterisation is it must be distinct from training (and obviously) testing. It allows parameter tuned without overtightening. Lasso regression will require parameterisation.

I don't know about the survival regressions for machine learning, but lasso/ridge regression are standard approaches.