similar to: Need Help in K-fold validation in Decision tree

Dear R user, I am looking for a code on double cross validation in LASSO , one for optimizing the parameter and other one is for MSEP. If any one have it, please foroward to me. I am using different package like LARS, chemometric etc. Thanks in advance Alex [[alternative HTML version deleted]]

Hi   I want to do leave-one-out cross-validation for multinomial logistic regression in R. I did multinomial logistic reg. by package nnet in R. How I do validation? by which function? response variable has 7 levels   please help me   Thanks alot Azam [[alternative HTML version deleted]]

Hello, I've been very impressed by the reshape package and how easy it makes reorganizing statistical data structures. This makes me wonder if there's another package out there that addresses another set of tasks that one often does when preparing data for analysis. For any particular set of analyses, one typically recodes variables and deletes cases and variables. It would be really

Hi, Users, I am a new user. I am trying to partition data into training and test. Is there any R package or function that can partition dataset? Also, is there any package do crossvalidation? Any help will be appreciated. Best, Pat [[alternative HTML version deleted]]

Dear R expert I have come across the GBM package for R and it seemed appropriate for my research. I am trying to predict the number of FPGA resources required by a Software Function if it were mapped onto hardware. As input I use software metrics (a lot of them). I already use several regression techniques, and the graphs I produce with GBM look promising. Now my question... I see that the

Dear list members. I have problems with the usage of cv.glm from the boot package. Here are some parts of the script I wanted to use: data <- read.table("selected_2D.csv", header=TRUE, sep=",") ? glm.fitted <- glm("ydata$ y ~ 1 + density + vsurf_ID6 + vsurf_S ", data=data) error <- cv.glm(data=data, glm.fitted, K=6) ydata$y is a separate data set, where

hi there!! i am trying to implement the code of the e1071 package for naive bayes, but it doens't really work, any ideas?? i am very glad about any help!! i need a naive bayes with 10-fold cross validation: code: library(e1071) model <- naiveBayes(code ~ ., mydata) tune.control <- tune.control(random = FALSE, nrepeat = 1, repeat.aggregate = min, sampling = c("cross"),

The "tscount" package (see http://doi.org/10.18637/jss.v082.i05) comes with several count data time series. Maybe this is the kind of discrete data you were interested in? hth, Z On Tue, 2 Jan 2018, Eric Berger wrote: > Hi Rolf, > I looked at > https://docs.microsoft.com/en-us/azure/sql-database/sql-database-public-data-sets > > One of the first sets in the list is

On Mon, Feb 26, 2018 at 3:25 PM, Gary Black <gwblack001 at sbcglobal.net> wrote: (Sorry to be a bit slow responding.) You have not supplied a complete example, which would be good in this case because what you are suggesting could be a serious bug in R or a package. Serious journals require reproducibility these days. For example, JSS is very clear on this point. To your question >

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

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,

Version 4.63 of the caret package is now on 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 99 different models for classification and regression. See the package vignettes

Version 4.63 of the caret package is now on 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 99 different models for classification and regression. See the package vignettes

Version 4.39 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 75 different models for classification and regression. See the package

Version 4.39 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 75 different models for classification and regression. See the package

Hi, I have data set where i have col1,col2,col3,col4 i want to write a condition where the rows has to removed from the dataset for col1>10 Please help, Thanks Santosh -- View this message in context: http://r.789695.n4.nabble.com/Removing-the-rows-from-dataset-tp4585710p4585710.html Sent from the R help mailing list archive at Nabble.com. [[alternative HTML version deleted]]

Hi All, I am looking for a package that performs Repeated k-fold cross-validation with Stepwise Regression. I would greatly appreciate if someone could share with us a package(s) that include this type of analysis. Thank you very much in advance. Chris [[alternative HTML version deleted]]

Hi all, Has anyone tried to do k-fold cross validation for flexible discriminant analysis ( mda library), for example, using crossval() in bootstrap? The problem is that the function crossval() requires a separate matrix for predictors and another for responses, whereas the function fda(), using the formula argument only. Is there another way of doing k-fold cross validation for functions which

The following helps identify when .GlobalEnv$.Random.seed has changed: rng_tracker <- local({ last <- .GlobalEnv$.Random.seed function(...) { curr <- .GlobalEnv$.Random.seed if (!identical(curr, last)) { warning(".Random.seed changed") last <<- curr } TRUE } }) addTaskCallback(rng_tracker, name = "RNG tracker") EXAMPLE: >