similar to: Caret package and lasso

I'm using caret to assess classifier performance (and it's great!). However, I've found that my results differ between R2.* and R3.* - reported accuracies are reduced dramatically. I suspect that a code change to kernlab ksvm may be responsible (see version 5.16-24 here: http://cran.r-project.org/web/packages/caret/news.html). I get very different results between caret_5.15-61 +

Hi, I am trying to construct a svmpoly model using the "caret" package (please see code below). Using the same data, without changing any setting, I am just changing the seed value. Sometimes it constructs the model successfully, and sometimes I get an ?Error in indexes[[j]] : subscript out of bounds?. For example when I set seed to 357 following code produced result only for 8

Dear All, I am using "Caret"package for SVM regression and elastic net regression . I can get the final fiited vs observed values. How can I get the coefficients? Any ideas? Thanks Alex [[alternative HTML version deleted]]

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

Say I train a model in caret, e.g.: RFmodel <- train(X,Y,method='rf',trControl=myCtrl,tuneLength=1) How can I save this to disk and load it later in R? How about an object of the class "resamples"? resamps <- resamples( list( RF = RFmodel, SVM = SVMmodel, KNN = KNNmodel, NN = NNmodel )) Thanks,

When I try to crate a grid of parameters for training with caret I get various errors: ------------------------------------------------------------ > my_grid <- createGrid("rf") Error in if (p <= len) { : argument is of length zero > my_grid <- createGrid("rf", 4) Error in if (p <= len) { : argument is of length zero > my_grid <-

Hello, I am learning caret package, and I want to use the RFE to reduce the feature. I want to use RFE coupled Random Forest (RFE+FR) to complete this task. As we know, there are a number of pre-defined sets of functions, like random Forest(rfFuncs), however,I want to tune the parameters (mtr) when RFE, and then I write code below, but there is something wrong message, How can I deal with it?

Hi, I am a graduate student applying published R scripts to compare the classification accuracy of 2 predictive models, one built using discriminant function analysis and one using random forests (webpage link for these scripts is provided below). The purpose of these models is to predict the biotic integrity of streams. Specifically, I am trying to compare the classification accuracy (i.e.,

Hi, I am having problems passing arguments to method="gbm" using the train() function. I would like to train gbm using the laplace distribution or the quantile distribution. here is the code I used and the error: gbm.test <- train(x.enet, y.matrix[,7], method="gbm", distribution=list(name="quantile",alpha=0.5), verbose=FALSE,

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)

Dear all, I want to train my model with LASSO using caret package (glmnet). So, in glmnet, there are two parameters, alpha and lambda. How can I fix my alpha=1 to get a lasso model? con<-trainControl(method="cv",number=10) model <- train(X, y, "glmnet", metric="RMSE",tuneLength = 10, trControl = con) Thanks Alex Roy [[alternative HTML

I am used to packages like e1071 where you have a tune step and then pass your tunings to train. It seems with caret, tuning and training are both handled by train. I am using train and trainControl to find my hyper parameters like so: MyTrainControl=trainControl( method = "cv", number=5, returnResamp = "all", classProbs = TRUE ) rbfSVM <- train(label~., data =

Hi Sacha, On second thought, perhaps this is more the direction that you want ... X2 = cbind(X_train,y_train) colnames(X2)[3] = "y" regr2<-randomForest(y~x1+x2, data=X2,maxnodes=10, ntree=10) regr regr2 #Make prediction predictions= predict(regr, X_test) predictions2= predict(regr2, X_test) HTH, Eric On Tue, May 9, 2023 at 6:40?AM Eric Berger <ericjberger at gmail.com>

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,

Dear R users, I have problem with extracting coefficients from a object. Here, X (predictor)and Y (response) are two matrix , I am regressing X ( dimensions 10 x 20) on each of columns of Y[,1] (10 x 1) and want to store the coefficient values. I have performed a Elastic Net regression and I want to store the coeffcients in each iteration. I got an error message . I do not

I have a binary classification problem where the fraction of positives is very low, e.g. 20 positives in 10,000 examples (0.2%) What is an appropriate cross validation scheme for training a classifier with very few positives? I currently have the following setup: ======================================== library(caret) tmp <- createDataPartition(Y, p = 9/10, times = 3, list = TRUE)

Dear R users,   I am new user for elastic net. I am trying to use elasticnet library. I have marker data with 359 markers and 168 samples, and response is metabolites. I am trying to do regression between a metabolite and markers.  But i am getting the following error:   > en<-enet(marker,as.numeric(vio),lambda=0.5,normalize=FALSE,intercept=TRUE) Error in one %*% x : requires numeric

<!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:

Dear Ladies and Gentlemen, I have several old, yet functioning APC Smart-UPS (Model 600LS, Part AP600I, Firmware 6JI, no EEPROM, from 1991). They are not supported by Powerchute any more. I run NUT 2.6.5-4 on Windows XP SP3. I use COM1 port with cable 940-0024C. The connection fails with APC Smart protocol driver 3.0 (2.6.3-3534:3540M), APC command table version 3.0 or higher - it works with APC

Dear list, I am trying to create a three-way table with percent occurrence instead of raw frequencies. However, I cannot get the results I expected: I have the following table: > ftable(table( mannerDF$agem, mannerDF$target, mannerDF$manner )) <snip> 50 bak 0 0 0 0 1 0 pak 0 0 0 0 3 0 sak