Thanks a lot! After thinking about it it definitely makes sense !
Greetings, Matthias
--
View this message in context:
http://r.789695.n4.nabble.com/Only-one-class-shown-in-SVM-plot-tp4637782p4638629.html
Sent from the R help mailing list archive at Nabble.com.
___
Ok, here a simple example. The file
http://r.789695.n4.nabble.com/file/n4637924/test.csv test.csv has 400 lines
containing 20 columns (1. column is class label, the other 19 are the
features).
So what I'm doing is
/
data <- read.csv(file="test.csv", head=F, sep=",")
names(data) <- c("Class","V1
Thank you!
Still not clear...I can plot two of my data dimensions against each other
where I see two separated clouds. So it must be possible to determine the
coefficients of the dividing line from the model I get from lda(...), or am
I completely wrong now...?
--
View this message in context:
Hello users!
I'm calculating a simple model using svm(...) from the e1071 package. So far
so good, with a linear kernel I'm getting 5 SVs. When plotting the result I
see very well separated data clouds, but the underlying color is constantly
pink, so as far as I understand no class separation is sh
Dear Users!
I think I still have some problems in understanding LDA and the methods of
plotting the results.
The case is the following: I'm having a dataset containing two classes where
each datapoint has 19 dimensions. Training with lda(...) works fine, and I'm
getting 19 LD coefficients. So far s
Thanks! For testing purposes this rescaling works! But unfortunately due to
timing constraints I'm not able to do the rescaling of the data, so as I
mentioned I have to work on with unscaled data. So I have to calculate
$f(\vec x) = sum_{i \in sv} coefs_i \langle \vec x_i \cdot \vec x \rangle -
\rh
Dear Community!
I'm using the svm method of package e1071 for classifying my data. This
really works fine, but however I have to work after creating the support
vectors and the parameters with unscaled data. So the problem is when I try
to train the classifier with the option "scale=F" the result i
7 matches
Mail list logo