On Thu, Sep 13, 2012 at 10:15 AM, Vignesh Prajapati <vign...@tatvic.com> wrote: > Hello, > > After development of recommendation engine with the R, before removal of > outliers from data-set value of residual standard error was 1351 and after > removal of outlier its 100. Still there is no accurate prediction which > gives 10% correct(near) prediction. For more fitting i also have tried > polynomial model with two ,three and four degree but still no improvement. > Is there any most important thing to consider without R-squared or adjusted > R-squared. > > Where i am using dataset with linear regression model for prediction of > product purchase revenue on the base of total numbers of time product added > to cart, removed from cart, total numbers of page views of product page. > For checking model prediction accuracy i am considering only minimum > residual standard error. > > Thanks > > Vignesh
Hi Vignesh, As described, your problem is quite hard for me to understand: perhaps you could work up a reproducible example as suggested here: http://stackoverflow.com/questions/5963269/how-to-make-a-great-r-reproducible-example Cheers, Michael ______________________________________________ R-help@r-project.org mailing list 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.
