How I can use majority guessing function to evaluate KNN, if I have data saved
in CSV file
Alnazer Elbedairy
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PLEASE do read the posting
Appreciated Jim.
Alnazer Elbedairy
> On Feb 25, 2016, at 4:15 AM, Jim Lemon wrote:
>
> Hi Alnazer,
> I'm not surprised that it didn't do what you expected. Even if I clean
> up the code so that it will actually run:
>
> majorityGuessing<-function(trainingDat
dear there
what the steps I can find the knn by using majority guessing.
thanks.
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PLEASE do rea
Dear all
anyone know the function or syntax to get the Normalization for Data ?
thanks
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PLEASE
wrote:
> Hi Alnazar,
> I looked at your question yesterday and was unable to find what a
> "majority guessing" function is. I think it may be related to the
> "Pandemonium" model of decision making, but that doesn't get me very
> far. Could you give us a hint a
2 AM, Ben Tupper wrote:
> Hi,
>
> Do the knn() or knn1() functions in the 'class' package serve your purpose?
>
> https://cran.r-project.org/web/packages/class/index.html
>
> Ben
>
> > On Feb 25, 2016, at 9:49 AM, David L Carlson wrote:
> >
> > P
Dear All
I did the following steps to get a PDF file from Rstudio
1- activate Rmarkdown
2- save file as (name.Rmd)
3- use chunk for each step
4- go to Knit - PDF to save a file as PDF but I got an error
any help please
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_
i rather than Chrome _strongly_ recommended)
Linux: Use system package manager
On Thu, Feb 25, 2016 at 3:58 PM, Erin Hodgess
wrote:
> What's the error? You have to show it and the Rmd too, please.
>
>
>
> On Thu, Feb 25, 2016 at 5:35 PM, Alnazer Elbedairy <
> alnaz
installed, but you have to install TEXmaker too, it works now, thank you
guys
On Thu, Feb 25, 2016 at 11:10 PM, Thomas Petzoldt wrote:
> Yes, you are right. Sweave depends on Latex too, so its no workaround in
> this case.
>
> Hope it helps, thpe
>
> Am 26.02.2016 um 06:54 schrieb Ulrik Stervbo:
dear all
attached you will find a csv datasets, there are many steps before these
they work properly. but I have errors in these steps I guess. any help
appreciated.
Step1: convert the data from continuous to categorical
##nautodata is the normalized data. I did it in the previous steps.
MPGCat=
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