[R] How to use preProcess in Caret?
bur@k@ym@kc| @end|ng |rom gm@||@com
Thu Dec 5 11:21:45 CET 2019
Yes, I'd tried scale as well. I mean, I could do my preprocessing
separately and it was working fine.
I was just wondering how preProcess argument in train function works. As
far as I know, when preProcess argument is set, it normalizes inputs but
Then I've figured we could also use recipes and that normalizes both
predictors and outcomes as you wish.
you can take a look at the question I've asked on SO.
You can see the use of recipe in comments below by "missuse".
I will read the link you've shared as well.
William Michels <wjm1 using caa.columbia.edu>, 4 Ara 2019 Çar, 21:04 tarihinde
> Have you tried alternative methods of pre-processing your data, such
> as simply calling scale()? What is the effect on convergence, for both
> the caret package and and the neuralnet package? There's an example
> using scale() with the neuralnet package at the link below:
> HTH, Bill.
> W. Michels, Ph.D.
> On Sun, Dec 1, 2019 at 10:04 AM Burak Kaymakci <burakaymakci using gmail.com>
> > Hello there,
> > I am using caret and neuralnet to train a neural network to predict times
> > table. I am using 'backprop' algorithm for neuralnet to experiment and
> > learn.
> > Before using caret, I've trained a neuralnet without using caret, I've
> > normalized my input & outputs using preProcess with 'range' method. Then
> > predicted my test set, did the multiplication and addition on predictions
> > to get the real values. It gave me good results.
> > What I want to ask is, when I try to train my network using caret, I get
> > error saying algorithm did not converge. I am thinking that I might be
> > doing something wrong with my pre-processing,
> > How would I go about using preProcess in train?
> > Do I pass my not-normalized data set to the train function and train
> > function handles normalization internally?
> > You can find my R gist here
> > <https://gist.github.com/andreyuhai/f299282f5a827e2a27c586afc9eb4eb5>
> > Thank you,
> > Burak
> > ______________________________________________
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