This is a statistical question, not actually a question about R, and thus not on topic. Using too many variables leads to models that tend to have large errors on new data (not from your fitting sample). [1]
[1] https://en.m.wikipedia.org/wiki/Overfitting On April 19, 2023 6:50:44 AM PDT, akshay kulkarni <akshay...@hotmail.com> wrote: >Dear members, > I am doing some modelling with caret package in R. I > do suppose that the package doesn't consider AIC and BIC for model selection, > right? They penalise the number of prameters, but I am ready to spend a > little more time and a little more money to run the model with more number of > parameters, but with a very less SSE. > >I know that AIC and BIC are not meant for non parametric ML models, but just >want to confirm.... > >Thanking you, >Yours sincerely, >AKSHAY M KULKARNI > > [[alternative HTML version deleted]] > >______________________________________________ >R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see >https://stat.ethz.ch/mailman/listinfo/r-help >PLEASE do read the posting guide http://www.R-project.org/posting-guide.html >and provide commented, minimal, self-contained, reproducible code. -- Sent from my phone. Please excuse my brevity. ______________________________________________ R-help@r-project.org mailing list -- To UNSUBSCRIBE and more, see https://stat.ethz.ch/mailman/listinfo/r-help PLEASE do read the posting guide http://www.R-project.org/posting-guide.html and provide commented, minimal, self-contained, reproducible code.
