Displaying 20 results from an estimated 300 matches similar to: "caret - prevent resampling when no parameters to find"
2011 Mar 18
2
Need help with error
Hi R users,
I am getting the following error when using the splsda function in R
v2.12.1:
"Error in switch(classifier, logistic = { : EXPR must be a length 1
vector"
What does this mean and how do I fix this?
Thank you in advance!
Best,
Savi
2013 Jan 29
1
starting values in glm(..., family = binomial(link = log))
Dear R-helpers,
i have a problem with a glm-model. I am trying to fit models with the log as link function instead of the logit. However, in some cases glm fails to estimate those models and suggests to give start values. However, when I set start = coef(logistic_model) within the function call, glm still says it cannot find starting values? This seems to be more of a problem, when I include a
2012 Apr 06
0
resampling syntax for caret package
Max and List,
Could you advise me if I am using the proper caret syntax to carry out
leave-one-out cross validation. In the example below, I use example
data from the rda package. I use caret to tune over a grid and select
an optimal value. I think I am then using the optimal selection for
prediction. So there are two rounds of resampling with the first one
taken care of by caret's train
2007 Apr 30
0
[999] branches/wxruby2/wxwidgets_282/samples/caret/caret.rb: Use paint() instead of ClientDC.new() in caret sample
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
"http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head><meta http-equiv="content-type" content="text/html; charset=utf-8" /><style type="text/css"><!--
#msg dl { border: 1px #006 solid; background: #369; padding:
2011 Aug 28
1
Trying to extract probabilities in CARET (caret) package with a glmStepAIC model
Dear developers,
I have jutst started working with caret and all the nice features it offers. But I just encountered a problem:
I am working with a dataset that include 4 predictor variables in Descr and a two-category outcome in Categ (codified as a factor).
Everything was working fine I got the results, confussion matrix etc.
BUT for obtaining the AUC and predicted probabilities I had to add
2007 Jul 16
0
[1113] trunk/wxruby2/samples/caret/caret.rb: Ensure the canvas has focus so KeyEvents are directed to it, fixing bug 10663
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
"http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head><meta http-equiv="content-type" content="text/html; charset=utf-8" /><style type="text/css"><!--
#msg dl { border: 1px #006 solid; background: #369; padding:
2007 Jul 21
0
[1130] trunk/wxruby2/swig/classes/Caret.i: Make Caret managed as object, so it is cleaned up properly when not
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
"http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head><meta http-equiv="content-type" content="text/html; charset=utf-8" /><style type="text/css"><!--
#msg dl { border: 1px #006 solid; background: #369; padding:
2007 Apr 29
0
[981] branches/wxruby2/wxwidgets_282/samples/caret/caret.rb: Don''t call PaintDC.new; just refresh() instead of duplicating paint code
<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.1//EN"
"http://www.w3.org/TR/xhtml11/DTD/xhtml11.dtd">
<html xmlns="http://www.w3.org/1999/xhtml">
<head><meta http-equiv="content-type" content="text/html; charset=utf-8" /><style type="text/css"><!--
#msg dl { border: 1px #006 solid; background: #369; padding:
2011 Jun 22
1
caret's Kappa for categorical resampling
Hello,
When evaluating different learning methods for a categorization problem with
the (really useful!) caret package, I'm getting confusing results from the
Kappa computation. The data is about 20,000 rows and a few dozen columns,
and the categories are quite asymmetrical, 4.1% in one category and 95.9% in
the other. When I train a ctree model as:
model <- train(dat.dts,
2007 May 07
0
[ wxruby-Bugs-10663 ] Keys not responding in caret sample, Linux
Bugs item #10663, was opened at 2007-05-07 19:57
You can respond by visiting:
http://rubyforge.org/tracker/?func=detail&atid=218&aid=10663&group_id=35
Category: Incorrect behavior
Group: None
Status: Open
Resolution: None
Priority: 3
Submitted By: Alex Fenton (brokentoy)
Assigned to: Alex Fenton (brokentoy)
Summary: Keys not responding in caret sample, Linux
Initial Comment:
On
2007 Mar 06
0
[ wxruby-Bugs-9093 ] Caret sample segfault on exit on Windows
Bugs item #9093, was opened at 2007-03-06 14:46
You can respond by visiting:
http://rubyforge.org/tracker/?func=detail&atid=218&aid=9093&group_id=35
Category: Incorrect behavior
Group: None
Status: Open
Resolution: None
Priority: 3
Submitted By: Nobody (None)
Assigned to: Kevin Smith (qualitycode)
Summary: Caret sample segfault on exit on Windows
Initial Comment:
The caret sample
2006 Aug 22
1
Patch for caret.rb
I removed all the ()''s and patched things so it won''t crash on bad
keystrokes.
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wxruby-users mailing list
wxruby-users@rubyforge.org
http://rubyforge.org/mailman/listinfo/wxruby-users
2005 Aug 16
1
Patch for caret.rb
This patch fixes the following errors:
1. Crash when you resize smaller than 1 line
2. Crash when you resize the cursor outside the screen
3. Doesn''t clear screen when you resize (on Windows at least)
_______________________________________________
wxruby-users mailing list
wxruby-users@rubyforge.org
http://rubyforge.org/mailman/listinfo/wxruby-users
2010 Apr 06
1
Caret package and lasso
Dear all,
I have used following code but everytime I encounter a problem of not having
coefficients for all the variables in the predictor set.
# code
rm(list=ls())
library(caret)
# generating response and design matrix
X<-matrix(rnorm(50*100),nrow=50)
y<-rnorm(50*1)
# Applying caret package
con<-trainControl(method="cv",number=10)
data<-NULL
data<- train(X,y,
2011 May 28
0
how to train ksvm with spectral kernel (kernlab) in caret?
Hello all,
I would like to use the train function from the caret package to
train a svm with a spectral kernel from the kernlab package. Sadly
a svm with spectral kernel is not among the many methods in caret...
using caret to train svmRadial:
------------------
library(caret)
library(kernlab)
data(iris)
TrainData<- iris[,1:4]
TrainClasses<- iris[,5]
set.seed(2)
2011 Mar 15
0
wxStyledTextCtrl: how to set caret at begining of a selection?
Hi there,
I was trying to select text backwards in a StyledTextCtrl, i.e, want the
caret to be in the front, not at the end of selection, however
unsucessful.
My code like this:
set_selection_end(11)
set_selection_start(5)
Any suggestions? thanks in advance.
Zhimin
--
Posted via http://www.ruby-forum.com/.
2012 Feb 10
1
Choosing glmnet lambda values via caret
Usually when using raw glmnet I let the implementation choose the
lambdas. However when training via caret::train the lambda values are
predetermined. Is there any way to have caret defer the lambda
choices to caret::train and thus choose the optimal lambda
dynamically?
--
Yang Zhang
http://yz.mit.edu/
2008 Sep 06
0
New caret packages
New major versions of the caret packages (caret 3.37, caretLSF 1.23 and
caretNWS 0.23) have been uploaded to CRAN.
caret is a package for building and evaluating a wide variety of predictive
models. There are functions for pre-processing, tuning models using
resampling, visualizing the results, calculating performance and estimating
variable importance. caretNWS and caretLSF are two parallel
2008 Sep 06
0
New caret packages
New major versions of the caret packages (caret 3.37, caretLSF 1.23 and
caretNWS 0.23) have been uploaded to CRAN.
caret is a package for building and evaluating a wide variety of predictive
models. There are functions for pre-processing, tuning models using
resampling, visualizing the results, calculating performance and estimating
variable importance. caretNWS and caretLSF are two parallel
2011 May 01
0
Dummy variables using rfe in caret for variable selection
I'm trying to run "rfe" for variable selection in the caret package, and am
getting an error. My data frame includes a dummy variable with 3 levels.
x <- chlDescr
y <- chl
#crate dummy variable
levels(x$State) <- c("AL","GA","FL")
dummy <- model.matrix(~State,x)
z <- cbind(dummy, x)
#remove State category variable
w <- z[,c(-4)]