Message-ID: <CAAmySGO=LK2PLyXN3AOP2_328G8qfgqhaZU7TYN3naNRWhV-cw@mail.gmail.com>
Date: 2012-09-13T10:23:48Z
From: R. Michael Weylandt
Subject: Inaccurate prediction in R
In-Reply-To: <CAOmTZ0j+uREX5KVFfxFCwwPdBp8cOfoc=czn9FTof5fp+YZ1bg@mail.gmail.com>
On Thu, Sep 13, 2012 at 10:15 AM, Vignesh Prajapati <vignesh at 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