similar to: Bagging with SVM

Dear sir,I have a data set which name is "c78p",now I want to deal with it with bagging. % of data from c78p as training set is 50%,and number of bootstrap random samples with replacement from c78p is 5,use SVM in each run,can you help me to write the code?Thanks.Best regards,Jie _________________________________________________________________ [[alternative HTML version

I was trying to produced bagged importance estimates of attributes in earth using the caret package with the following commands:   fit2 <- bagEarth(loyalty ~ ., data=model1, B = 10)   bagImpGCV <- varImp(fit2,value="gcv") My bootstrap estimates are produced however the second command "varImp" produces the following error:    Error in UseMethod("varImp") : no

Good morning all. I am doing bagging with package caret. I need bagging for a classification problem. I am working with " bag". bag(x, y, B = 10, vars = NULL, bagControl = bagControl(), ...) bagControl(fit = NULL, predict = NULL, aggregate = NULL, downSample = FALSE) My fit function is: svmFit <- function(x, y, ...) { library(e1071)

Hello! Does anyone know how to apply bagging for SVM? ( for example) I am using adabag package to execute bagging but this method, "bagging", works with classification trees. I would like to apply my bagging to other classifiers as SVM,RNA or KNN. Has anyone do it? Thanks!! [[alternative HTML version deleted]]

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 <-

I am using bagging to perform Bagged Regression Trees on count data (bird abundance in Britain and Ireland, in relation to climate and land cover variables). Predictions from the final model are visually believable but I would really like a diagnostic equivalent to classification success that can be used to decide if a model is adequate. Whereas with classification data an error rate is returned,

Hi everyone I am trying to do cross validation (10 fold CV) by using e1071:svm method. I know that there is an option (?cross?) for cross validation but still I wanted to make a function to Generate cross-validation indices using pls: cvsegments method. ##################################################################### Code (at the end) Is working fine but sometime caret:confusionMatrix

Hi all, I've been using a custom summary function to optimise regression model methods using the caret package. This has worked smoothly. I've been using the default bootstrapping resampling method. For bagging models (specifically randomForest in this case) caret can, in theory, uses the out-of-bag (oob) error estimate from the model instead of resampling, which (in theory) is largely

*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. [[alternative HTML version deleted]]

Dear Sir, I got a problem with my program. I would like to classify my data using bagging support vector machine ensemble. I split my data into training data and test data. For a given data sets TR(X), K replicated training data sets are first randomly generated by bootstrapping technique with replacement. Next, Support Vector Mechine (SVM) is applied for each bootstrap data sets. Finally, the

Hola, Puedes poner un RStudio en Amazon, poner "caret" y a correr.... No sé si tendrás suficiente con lo que te pueda ofrecer Amazon para tu problema... creo que sí... ;-).... O directamente hacerlo aquí, que toda esta instalación ya la tienen hecha: http://www.teraproc.com/front-page-posts/r-on-demand/ Gracias, Carlos. El 10 de diciembre de 2015, 14:43, MªLuz Morales <mlzmrls

Estimados Un día leí algo en el siguiente hipervínculo, pero nunca lo use. http://blog.revolutionanalytics.com/2015/06/using-hadoop-with-r-it-depends.html Javier Rubén Marcuzzi De: Carlos J. Gil Bellosta Enviado: miércoles, 9 de diciembre de 2015 14:33 Para: MªLuz Morales CC: r-help-es Asunto: Re: [R-es] SVM hadoop No, no correrán en paralelo si usas los SVM de paquetes como e1071. No

Dear list I've a generic question about how to tune an SVM I'm trying to classify with caret package some population data from a case-control study . In each column of my matrix there are the SNP genotypes , in each row there are the individuals. I correctly splitted my total dataset in training(132 individuals) and test (50 individuals) (respecting the total observed genotypic

Hola Mª Luz, Te cuento un poco mi visión: Lo primero de todo es tener claro qué quiero hacer exactamente en paralelo, se me ocurren 3 escenarios: (1) Aplicar un modelo en este caso SVM sobre unos datos muy grandes y por eso necesito hadoop/spark (2) Realizar muchos modelos SVM sobre datos pequeños (por ejemplo uno por usuario) y por eso necesito hadoop/spark para parelilizar estos procesos

Hello, Is the first time I am using SNOW package and I am trying to tune the cost parameter for a linear SVM, where the cost (variable cost1) takes 10 values between 0.5 and 30. I have a large dataset and a pc which is not very powerful, so I need to tune the parameters using both CPUs of the pc. Somehow I cannot manage to do it. It seems that both CPUs are fitting the model for the same values

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?

Good afternoon. I would like to know if there is any function in R to do LOOCV with these classifiers: 1)SVM 2)Neural Networks 3)C4.5 ( J48) 4)KNN Thanks a lot! [[alternative HTML version deleted]]

Hi, Is here anyone knows the difference between rapart and bagging when grow a CART tree? Thanks Qin

Hi there, I am interested in using R for machine learning (supervised classification). Currently, I have been investigating especially the rpart, tree, and randomForest package, and have achieved first results. are there any experiences, how the learned classificators could be used in e.g. C ? in other words, I want to "transfer" the learned predictor from R to C-code. for e.g. rpart,

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 +