similar to: Bagging

Dear sir, If I want to use bagging with SVM, which package should I choose?Thanks! Best wishes,Jie _________________________________________________________________ [[alternative HTML version deleted]]

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]]

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)

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

Dear Sir, I am a R beginning user. I would like to apply the bagging algorithm to my data in order to classify a certain disease by Bagging Support Vector Machine Ensemble. My problem is that even if I am reading the book, looking at the examples in internet and available in R, learning a lot of theoretical things on about SVM esemble or Bagging method, I can't apply Bagged SVM esemble or

Hi what would you recommend to compare classification methods such as LDA, classification trees (rpart), bagging, SVM, etc: 10-fold cv (as in Ripley p. 346f) or leaving-one-out (as e.g. implemented in LDA)? my data-set is not that huge (roughly 200 entries) many thanks for a hint Christoph -- Christoph Lehmann <christoph.lehmann at gmx.ch>

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

Hi, I'm analysing some anthropometric data on fifty odd skull bases. We know the gender of each skull, and we are trying to develop a predictor to identify the sex of unknown skulls. Rpart with cross-validation produces two models - one of which predicts gender for Males well, and Females poorly, and the other does the opposite (Females well, and Males poorly). In both cases the error

Hi there. I was wondering if somebody knows how to perform a bagging procedure on a classification tree without running the classifier with weights. Let me first explain why I need this and then give some details of what I have found out so far. I am thinking about implementing the bagging procedure in Matlab. Matlab has a simple classification tree function (in their Statistics toolbox) but

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,

On Mon, Sep 14, 2020 at 05:48:07PM +0300, Dan Carpenter wrote: > Hi Jie, > > url: https://github.com/0day-ci/linux/commits/Jie-Deng/i2c-virtio-add-a-virtio-i2c-frontend-driver/20200911-115013 > base: https://git.kernel.org/pub/scm/linux/kernel/git/wsa/linux.git i2c/for-next > config: parisc-randconfig-m031-20200913 (attached as .config) > compiler: hppa-linux-gcc (GCC)

On Mon, Sep 14, 2020 at 05:48:07PM +0300, Dan Carpenter wrote: > Hi Jie, > > url: https://github.com/0day-ci/linux/commits/Jie-Deng/i2c-virtio-add-a-virtio-i2c-frontend-driver/20200911-115013 > base: https://git.kernel.org/pub/scm/linux/kernel/git/wsa/linux.git i2c/for-next > config: parisc-randconfig-m031-20200913 (attached as .config) > compiler: hppa-linux-gcc (GCC)

I'm having the most weird problem with bagging function. For some unknown reason it does not improve the classification (compared to rpart), but instead gives much worse results ! Running rpart on my data gives error rate of about 0.3 and bagging, instead of improving this results, gives error rate of 0.9 !!! I'm running both rpart and bagging with exactly the same parameters, I even

I wanted to report this issue here so others may not find themselves alone and as the author is apparently active on the list. I havent done an exhaustive test by any means, cause I dont have time. But here's a small example. Apparently the "ns" argument is the one that is killing it. I've gotten several different segfault messages, the only other one I remember said "out

I'm interested in further understanding the differences in using many classification trees to improve classification rates. I'm also interested in finding out what I can do in R and which methods will allow prediction. Can anybody point me to a citation or discussion? Specifically, I want to classify remotely sensed imagery where training data is extracted on class membership by the user.

Hi Craig, Thanks for the information. Can you point to the source that specifies tGPR to be R0 - R7? I tried to search in ARMInstrThumb.td but couldn’t find it. Thanks, - Jie On Apr 14, 2019, at 15:28, Craig Topper <craig.topper at gmail.com<mailto:craig.topper at gmail.com>> wrote: I believe there is probably a separate instruction in LLVM for thumb2 add. Probably starting with t2

Martin, Just a minor fix. Jie, Try the -r option (or the --recurse) option instead. It looks like the source changed to this: options.c: {"recurse", 'r', POPT_ARG_NONE, &recurse}, But the help is still at: options.c: rprintf(F," -r, --recursive recurse into directories\n"); eric Jie Gao wrote: > > rsync: --recursive:

Hello the code : set.seed(10) y<-c(1:1000) x1<-c(1:1000)*runif(1000,min=0,max=2) x2<-c(1:1000)*runif(1000,min=0,max=2) x3<-c(1:1000)*runif(1000,min=0,max=2) lm_fit<-lm(y~x1+x2+x3) summary(lm_fit) set.seed(10) all_data<-data.frame(y,x1,x2,x3) positions <- sample(nrow(all_data),size=floor((nrow(all_data)/4)*3)) training<- all_data[positions,] testing<-

Dear Sir, I am stuck with a nagging problem in using R for SVM regression. My data has 5 dimensions and 400 observations. The independent variables are : Peb, Ksub, Sub, and Xtt. The dependent variable is: Rexp. I tried using the svm.tune function as well as <_tune(svm.....), to tune the hyper parameters: gamma, epsilon and C. Since I am new to R, I am probably not using the svm.tune

Hi All, Here is the case. I want to build classification model (SVM). Some of variables for this model are categorical attributes which represent words (usually 3-10 words - query for search in google). For example: search_id | query_words |..| result -----------+----------------------------------+--+-------- 1 | how,to,grow,tree |..| 4 2