similar to: New caret packages

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

New versions of the caret (3.08) and caretLSF (1.12) packages have been released. caret (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for data splitting, pre-processing and model tuning, as well as other miscellaneous functions. In the new versions: - The elasticnet and the lasso (from the enet package)

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

Three more packages will be showing up on your mirror soon. The caret package (short for "Classification And REgression Training") aims to simplify the model building process. The package has functions for - data splitting: balanced train/test splits, cross-validation and bootstrapping sampling functions. There is also a function for maximum dissimilarity sampling. -

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

Version 4.06 of the caret package was sent to CRAN. caret can be used to tune the parameters of predictive models using resampling, estimate variable importance and visualize the results. There are also various modeling and "helper" functions that can be useful for training models. caret has wrappers to over 50 different models for classification and regression. See the package

Sorry for the tardy reply but I just found your posting incidentally today. To make long things short: You are right about the centering. We forgot to correct the intercept if center = TRUE. We lately found the problem ourself and fixed it in the current version (mboost 2.0-3). However the problem only occurred if you extracted the coefficients. As the intercept is rarely interpretable we

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

A new version of kernlab has just been released. kernlab is a kernel-based Machine Learning package for R. kernlab includes the following functions: o ksvm() : Support Vector Machines for classification, regression, novelty detection, native multi-class classification, support for class-probability output and confidence intervals in regression. o gausspr()

Hello, I have noticed a bug with LSSVM implementation in R. It could be a bug with the LSSVM itself that causes this problem. I thought I should post this message to see if anyone else is familiar with this problem and explain why the result is different for odd and even number of cases. Once the hyperplane is found using LSSVM, the prediction results vary when you predict odd or even number of

Hello, In my ongoing quest to develop a "best" model, I'm testing various forms of SVM to see which is best for my application. I have been using the SVM from the e1071 library without problem for several weeks. Now, I'm interested in RVM and LSSVM to see if I get better performance. When running RVM or LSSVM on the exact same data as the SVM{e1071}, I get an error that I

Hi there, I'm trying to do a regression experiment on a multidimensional dataset where both x and y in the model are multidimensional vectors. I'm using R version 2.9.2, updated packages, on a Linux box. I've tried gausspr(), ksvm() and rvm(), and the models are computed fine, but I'm always getting the same error message when I try to use predict(): "Error in

Dear useRs, I am struggling to use gamboost function form the 'mboost' package. More precisely, I am trying to extract the *partial fit* for each of the covariates estimated in a model and I usually end up with this annoying: "Error in newdata[[xname]] : subscript out of bounds ". I hope that the lack of details in my query can be straightforwardly compensated by examining the

Hi, I am using caretNWS on a RHEL x86_64 system and I am getting an error message that is nearly identical to the one occuring in http://www.r-project.org/nosvn/R.check/r-release-macosx-ix86/caretNWS-00check.txt Error in socketConnection(serverHost, port = port, open = "a+b", blocking = TRUE) : unable to open connection Calls: system.time ... .local -> tryCatch -> tryCatchList

Hi, I am using the caretNWS package to train some supervised regression models (gbm, lasso, random forest and mars). The problem I have encountered started when my training data set increased in the number of predictors and the number of observations. The training data set has 347 numeric columns. The problem I have is when there are more then 2500 observations the 5 sleigh objects start but do

Dear R-community, I was using SVM regression (svm {e1071}) for predictions of single soil properties of a huge data set (3000 samples). There are for the eps-regression using the radial basis kernel three optimization parameters needed. To make things easier (using only two optimization parameters and not loosing performance) I wanted to use LS SVM regression (lssvm{kernlab}). But it

Hi everybody, I''m using R on a 64-bit Ubuntu 9.04 (Jaunty). I prefer to install R packages from source, even if they are available in Synaptic. The problem is that I can''t install/compile kernlab. Everything works fine until it gets to the lazy loading part: ** preparing package for lazy loading Creating a new generic function for "terms" in "kernlab"

I'm getting started in R, and I'm trying to use one of the gradient boosting packages, mboost. I'm already installed the package with install.packages("mboost") and loaded it with library(mboost). My problem is that when I attempt to call glmboost, I get a message that " Error in glmboost() : no applicable method for "glmboost" ". Does anybody have

Hi I have a matrix with 30 observations and roughly 30000 variables, each obs belongs to one of two groups. With svm and slda I get into memory troubles ('cannot allocate vector of size' roughly 2G). PCA LDA runs fine. Are there any way to use the memory issue withe SVM's? Or can you recommend any other classification method for such huge datasets? P.S. I run suse 9.1 on a 2G RAM

Hi all, First of all, I'm a novice R user (less that a week), so perhaps my code isn't very efficient. Using the MBoost package I created a model using the following command and saved it to a file for later use: model <- gamboost(fpfm,data=SampleClusterData,baselearner="bbs") # Creating a model save(model,file="model.RData") # Saving a model After this, during a