Displaying 20 results from an estimated 20000 matches similar to: "Best SVM Performance measure?"
2007 Dec 27
1
(package e1071) SVM tune for best parameters: why they are different everytime i run?
Hi,
I run the following tuning function for svm. It's very strange that every
time i run this function, the best.parameters give different values.
[A]
>svm.tune <- tune(svm, train.x, train.y,
validation.x=train.x, validation.y=train.y,
ranges = list(gamma = 2^(-1:2),
cost = 2^(-3:2)))
# where train.x and train.y are matrix
2011 Jan 07
2
Stepwise SVM Variable selection
I have a data set with about 30,000 training cases and 103 variable.
I've trained an SVM (using the e1071 package) for a binary classifier
{0,1}. The accuracy isn't great.
I used a grid search over the C and G parameters with an RBF kernel to
find the best settings.
I remember that for least squares, R has a nice stepwise function that
will try combining subsets of variables to find
2009 Aug 30
1
SVM coefficients
Hello,
I'm using the svm function from the e1071 package.
It works well and gives me nice results.
I'm very curious to see the actual coefficients calculated for each
input variable. (Other packages, like RapidMiner, show you this
automatically.)
I've tried looking at attributes for the model and do see a
"coefficients" item, but printing it returns an NULL result.
2009 Aug 02
2
Strange column shifting with read.table
Hi,
I am reading in a dataframe from a CSV file. It has 70 columns. I do
not have any kind of unique "row id".
rawdata <- read.table("r_work/train_data.csv", header=T, sep=",",
na.strings=0)
When training an svm, I keep getting an error
So, as an experiment, I wrote the data back out to a new file so that I
could see what the svm function sees.
2009 May 11
1
Problems to run SVM regression with e1071
Hi R users,
I'm trying to run a SVM - regression using e1071 package but the function svm() all the time apply a classification method rather than a regression.
svm.m1 <- svm(st ~ ., data = train, cost = 1000, gamma = 1e-03)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1000
gamma: 0.001
Number of Support Vectors: 209
2017 Sep 02
0
problem in testing data with e1071 package (SVM Multiclass)
Hello all,
this is the first time I'm using R and e1071 package and SVM multiclass
(and I'm not a statistician)! I'm very confused, then. The goal is: I
have a sentence with sunny; it will be classified as "yes" sentence; I
have a sentence with cloud, it will be classified as "maybe"; I have a
sentence with rainy il will be classified as "no".
The
2006 Feb 28
3
does svm have a CV to obtain the best "cost" parameter?
Hi all,
I am using the "svm" command in the e1071 package.
Does it have an automatic way of setting the "cost" parameter?
I changed a few values for the "cost" parameter but I hope there is a
systematic way of obtaining the best "cost" value.
I noticed that there is a "cross" (Cross validation) parameter in the "svm"
function.
But I
2011 Feb 21
3
ROC from R-SVM?
*Hi,
*Does anyone know how can I show an *ROC curve for R-SVM*? I understand in
R-SVM we are not optimizing over SVM cost parameter. Any example ROC for
R-SVM code or guidance can be really useful.
Thanks, Angel.
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2003 Dec 10
3
e1071:svm - default epsilon = 0.1 (NOT 0.5) (PR#5671)
In e1071 package/svm default epsilon value is set to 0.1 and not 0.5
as documentation says.
R
2007 Dec 29
0
How to choose the best Kernel in SVM classification
Hi all,
I'm working with some data with 54 variables, 44 of them are binary, and I'm using SVM in package e1071 to carry out general classification but I don't know how to choose the best kernel for my data. I would be very grateful if someone could help me on this task.
Best regards,
Pedro Marques
2011 Aug 05
1
e1071 ver 1.5-27 and older - SVM bug report
Dear All:
I found a problem with the SVM internal cross-validation (CV) accuracy
estimation in the e1071 package.
File: Rsvm.c
Line: 120
Today, it is:
int j = rand()%(prob->l-i);
Should be:
int j = i + rand()%(prob->l-i);
The erroneous code doesn't shuffle objects. Instead, it "randomly"
moves objects from beginning to the end.
In hope for a prompt response from the
2017 Jul 06
0
svm.formula versus svm.default - different results
Dear community,
I'm performing svm-regression with svm at library e1071.
As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula
I've debugged both options.
While debugging the svm.formula, I've seen that when I reach the call:
ret <-
2010 Apr 29
2
can not print probabilities in svm of e1071
> x <- train[,c( 2:18, 20:21, 24, 27:31)]
> y <- train$out
>
> svm.pr <- svm(x, y, probability = TRUE, method="C-classification",
kernel="radial", cost=bestc, gamma=bestg, cross=10)
>
> pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)],
decision.values = TRUE, probability = TRUE)
> attr(pred, "decision.values")[1:4,]
2011 Jul 24
0
repeated execution of svm(e1071) gives different results, if probability = TRUE is set
Hello, Connoisseurs!
Please explain to novices, why svm model gives different results in the
loop with the same data? As a result, I can not find the best gamma and
cost parameters. Also tune.svm yields results that can not be repeated.
How can I avoid this?
My sessionInfo:
R version 2.11.1 (2010-05-31)
x86_64-pc-linux-gnu
locale:
[1] LC_CTYPE=en_US.UTF-8 LC_NUMERIC=C
2007 Feb 23
1
Repeated measures in Classification and Regresssion Trees
Dear R members,
I have been trying to find out whether one can use multivariate
regression trees (for example mvpart) to analyze repeated measures data.
As a non-parametric technique, CART is insensitive to most of the
assumptions of parametric regression, but repeated measures data raises
the issue of the independence of several data points measured on the
same subject, or from the same plot
2010 Apr 06
3
svm of e1071 package
Hello List,
I am having a great trouble using svm function in e1071 package. I have 4gb of data that i want to use to train svm. I am using Amazon cloud, my Amazon Machine Image(AMI) has 34.2 GB of memory. my R process was killed several times when i tried to use 4GB of data for svm. Now I am using a subset of that data and it is only 1.4 GB. i remove all unnecessary objects before calling
2006 Feb 16
2
getting probabilities from SVM
I am using SVM to classify categorical data and I would like the
probabilities instead of the classification. ?predict.svm says that its
only enabled when you train the model with it enabled, so I did that, but it
didn't work. I can't even get it to work with iris. The help file shows
that probability = TRUE when training the model, but doesn't show an
example. Then I try to
2012 Mar 02
1
e1071 SVM: Cross-validation error confusion matrix
Hi,
I ran two svm models in R e1071 package: the first without cross-validation
and the second with 10-fold cross-validation.
I used the following syntax:
#Model 1: Without cross-validation:
> svm.model <- svm(Response ~ ., data=data.df, type="C-classification",
> kernel="linear", cost=1)
> predict <- fitted(svm.model)
> cm <- table(predict,
2013 Jan 15
0
e1071 SVM, cross-validation and overfitting
I am accustomed to the LIBSVM package, which provides cross-validation
on training with the -v option
% svm-train -v 5 ...
This does 5 fold cross validation while building the model and avoids
over-fitting.
But I don't see how to accomplish that in the e1071 package. (I
learned that svm(... cross=5 ...) only _tests_ using cross-validation
-- it doesn't affect the training.) Can
2009 Jul 18
1
svm works but tune.svm give error
Hello,
I'm using the e1071 library for SVM functions.
I can quickly train an SVM with:
svm(formula = label ~ ., data = testdata)
That works well.
I want to tune the parameters, so I tried:
tune.svm(label ~ ., data=testdata[1:2000, ], gamma=10^(-6:3), cost=10^(1:2))
THIS FAILS WITH AN ERROR:
'names' attribute [199] must be the same length as the vector [184]
I don't