Hello,
I'm using the Bayesian model components (Learn Molecular Categories). There is the validation step with a Add leave-one-out cross-validation option. It removes one compound, build a model and then look at the score.
I was wondering if it would be possible to change this parameter (new options). So the user could decide the cross-validation parameters (10%, 20%...), which could be a simple random selection (implemented new methods could be longer and more difficult).
It is very convenient to have the information in the model, and the validation of multiples models at once would be great to see the quality by categories.
If you have to validate multpiple models to tell your chemists yes this one is good, but not this one, what is your best approach?
Jeremy
I'm using the Bayesian model components (Learn Molecular Categories). There is the validation step with a Add leave-one-out cross-validation option. It removes one compound, build a model and then look at the score.
I was wondering if it would be possible to change this parameter (new options). So the user could decide the cross-validation parameters (10%, 20%...), which could be a simple random selection (implemented new methods could be longer and more difficult).
It is very convenient to have the information in the model, and the validation of multiples models at once would be great to see the quality by categories.
If you have to validate multpiple models to tell your chemists yes this one is good, but not this one, what is your best approach?
Jeremy
