similar to: Cross Validation with SVM

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 list, Could someone help me to explain why the leave-one-out cross validation results I got from svm using the internal option "cross" are different from those I got manually? It seems using "cross" to do cross validation, the results are always better. Please see the code below. I also include lda as a comparison. I'm using WinXP, R-2.9.0, and e1071_1.5-19. Many

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

Hi, I ran two svm models in R e1071 package: the first without cross-validation and the second with 10-fold cross-validation. I used the following syntax: #Model 1: Without cross-validation: > svm.model <- svm(Response ~ ., data=data.df, type="C-classification", > kernel="linear", cost=1) > predict <- fitted(svm.model) > cm <- table(predict,

I am accustomed to the LIBSVM package, which provides cross-validation on training with the -v option % svm-train -v 5 ... This does 5 fold cross validation while building the model and avoids over-fitting. But I don't see how to accomplish that in the e1071 package. (I learned that svm(... cross=5 ...) only _tests_ using cross-validation -- it doesn't affect the training.) Can

1. This is _not_ a bug in R itself. Please don't use R's bug reporting system for contributed packages. 2. This is _not_ a bug in svm() in `e1071'. I believe you forgot to take sqrt. 3. You really should use the `tot.MSE' component rather than the mean of the `MSE' component, but this is only a very small difference. So, instead of spread[i] <- mean(mysvm$MSE), you

Full_Name: Noel O'Boyle Version: 2.1.0 OS: Debian GNU/Linux Sarge Submission from: (NULL) (131.111.8.96) (1) Description of error The 10-fold CV option for the svm function in e1071 appears to give incorrect results for the rmse. The example code in (3) uses the example regression data in the svm documentation. The rmse for internal prediction is 0.24. It is expected the 10-fold CV rmse

Hi: I am a newer in using R for data mining, and find the "e1071" pakage an excellent tool in doing data mining work! what frustrated me recently is that when I using the function "svm" and using the "cross=10" parameters, I got all the "accuracies" of the model greater than 1. Isn't that the accuracy should be smaller than 1? so I wander how, the

Dear all, Is it possible to extract cross-validation results from e1071's svm model? For example, the following R code shows the result from the 10 fold cross-validation. model = svm(spam ~ ., data = spam, cross = 10) summary(model) But, I could not figure out how to get to the accuracy values from the cross-validation. I looked at the svm method, but did not find any return values. Any

I am trying to understand what is the difference between linear and polynomial kernel: linear: u'*v polynomial: (gamma*u'*v + coef0)^degree It would seem that polynomial kernel with gamma = 1; coef0 = 0 and degree = 1 should be identical to linear kernel, however it gives me significantly different results for very simple data set, with linear kernel

hi, could you please tell me what kind of cross validation that SVM of e1071 uses? Cheers, Amy _________________________________________________________________ View photos of singles in your area! Looking for a hot date? [[alternative HTML version deleted]]

Dear All: I found a problem with the SVM internal cross-validation (CV) accuracy estimation in the e1071 package. File: Rsvm.c Line: 120 Today, it is: int j = rand()%(prob->l-i); Should be: int j = i + rand()%(prob->l-i); The erroneous code doesn't shuffle objects. Instead, it "randomly" moves objects from beginning to the end. In hope for a prompt response from the

I am trying to implement a simple r-svm example using the iris data (only two of the classes are taken and data is within the code). I am running into some errors. I am not an expert on svm's. If any one has used it, I would appreciate their help. I am appending the code below. Thanks../Murli ####################################################### ### R-code for R-SVM ### use leave-one-out

Hello guys! I am working with some classifiers ( SVM,C4.5,RNA,etc) using 10-C.V. Once I have the model of each one, I make the validation of these models in one dataset. Then,with my model and the dataset, I extract a confusion matrix to know the capacity of prediction from the model. And finally, I extract the accuracy of this prediction based on the diagonal from the confusion matrix. The

Hi My name is karthikeyan. I am using svm in R for my data set. my data set contain 60 finance ratio as variables and i want to classify into group of good and bad. I want to know how to do the crossvalidation for the svm . first i am doing modelling and i am predict and i am calculating the probabilities how can i do the cross validation and how can i plot the svm for this variables

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, This is probably going to be one of those, "It depends what you want" kind of answers, but I'm very curious to see if the group has an opinion or some general suggestions. The actual experiment is too complicated for a quick e-mail, but I'll summarize well enough(hopefully) to get the concepts across. Binary classification problem Using and SVM (e1071) to train a model

Hallo! I would need a code for 10-fold cross validation for the classifiers Naive Bayes and svm (e1071) package. Has there already been done something like that? I tried to do it myself by applying the tune function first: library(e1071) tune.control <- tune.control(random =F, nrepeat=1, repeat.aggregate=min.,sampling=c("cross"),sampling.aggregate=mean, cross=10, best.model=T,

Dear R users, There is a error message when I run the following code. It is used to load microarray data and use the top 1000 genes for training svm to classify test set . > library(e1071) Loading required package: class > f=read.table("F:\\lab\\ microarray analysis\\VEH LPS\\exprs.txt",

Hey! How do I get the confusion matrix after performing 10-fold cross validation from SVM in R? When I try to print it, I get the confusion matrix without cross validation. I need to compute PPV. Should I report PPV without CV and total accuracy with CV? I am confused. > svmtrain <- svm(xtrain,ytrain,kernel="sigmoid",cross=10) > pred <- predict(svmtrain, xtrain) >