Re: [R] caretNWS and training data set sizes

From: Max Kuhn <mxkuhn_at_gmail.com>
Date: Mon, 10 Mar 2008 12:41:12 -0400

What version of caret and caretNWS are you using? Also, what version of the nws server and twisted are you using? What kind of machine (# processors, how much physical memory etc)?

I haven't seen any real limitations with one exception: if you are running P jobs on the same machine, you are replicating the memory needs P times.

I've been running jobs with 4K to 90K samples and 1200 predictors without issues, so I'll need a lot more information to help you.

Max

On Mon, Mar 10, 2008 at 12:04 PM, Tait, Peter <ptait_at_skura.com> wrote:
> Hi,
>
> I am using the caretNWS package to train some supervised regression models (gbm, lasso, random forest and mars). The problem I have encountered started when my training data set increased in the number of predictors and the number of observations.
>
> The training data set has 347 numeric columns. The problem I have is when there are more then 2500 observations the 5 sleigh objects start but do not use any CPU resources and do not process any data.
>
> N=100 cpu(%) memory(K)
> Rgui.exe 0 91737
> 5x sleighs (RTerm.exe) 15-25 ~27000
>
> N=2500
> Rgui.exe 0 160000
> 5x sleighs (RTerm.exe) 15-25 ~74000
>
> N=5000
> Rgui.exe 50 193000
> 5x sleighs (RTerm.exe) 0 ~19000
>
>
> A 10% sample of my overall data is ~22000 observations.
>
> Can someone give me an idea of the limitations of the nws and caretNWS packages in terms of the number of columns and rows of the training matrices and if there are other tuning/training functions that work faster on large datasets?
>
> Thanks for your help.
> Peter
>
>
> > version
> _
> platform i386-pc-mingw32
> arch i386
> os mingw32
> system i386, mingw32
> status
> major 2
> minor 6.2
> year 2008
> month 02
> day 08
> svn rev 44383
> language R
> version.string R version 2.6.2 (2008-02-08)
>
> > memory.limit()
> [1] 2047
>
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>

-- 

Max

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Received on Mon 10 Mar 2008 - 16:45:46 GMT

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