similar to: Interpreting predictions of svm

If do: > library("e1071") > example(svm) I get: svm> data(iris) svm> attach(iris) svm> ## classification mode svm> # default with factor response: svm> model <- svm(Species ~ ., data = iris) svm> # alternatively the traditional interface: svm> x <- subset(iris, select = -Species) svm> y <- Species svm> model <- svm(x, y) svm>

svm.fit<-svm(as.factor(out) ~ ., data=all_h, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) # model fitting svm.pred<-predict(svm.fit, hh, decision.values = TRUE, probability = TRUE) # find the probability, but can not find. attr(svm.pred, "probabilities") > attr(svm.pred, "probabilities") 1 0 1 0 0 2 0

Hi all, Sorry to be bothering again with probably an easy error to fix, but I've been trying to solve the problem and haven't been able yet to do it. So I'm doing this: > dados<-read.table("b.txt",sep="",nrows=30000) >

> x <- train[,c( 2:18, 20:21, 24, 27:31)] > y <- train$out > > svm.pr <- svm(x, y, probability = TRUE, method="C-classification", kernel="radial", cost=bestc, gamma=bestg, cross=10) > > pred <- predict(svm.pr, valid[,c( 2:18, 20:21, 24, 27:31)], decision.values = TRUE, probability = TRUE) > attr(pred, "decision.values")[1:4,]

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(0)) attach(p5.new)

I run the following code, all other is ok, but plot(m.svm,p5.new,As~Cur) is not ok Anyone know why? install.packages("e1071") library(e1071) library(MASS) p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") p5.new<-subset(p5,select=-Ms) p5.new$Y<-factor(p5.new$Y) levels(p5.new$Y) <- list(Out=c(1), In=c(0)) attach(p5.new)

Hi all - I am trying to tune an SVM model by optimizing the cross-validation accuracy. Maximizing this value doesn't necessarily seem to minimize the number of misclassifications. Can anyone tell me how the cross-validation accuracy is defined? In the output below, for example, cross-validation accuracy is 92.2%, while the number of correctly classified samples is (1476+170)/(1476+170+4) =

Hello, I hope you can help me! I`m trying to tune svm parameters: cost and gamma for a landsat image classification, but I get an error and I can't understand it. I write this: > tune(svm, Class~., data = mdt01bis, ranges = list(gamma = 2^(-15:3), cost > = 2^(-5:15))) and R gives: Error en predict.svm(model, if (!is.null(validation.x)) validation.x else if (useFormula)

HI, GUYS, I used the following codes to run SVM and get prediction on new data set hh. dim(all_h) [1] 2034 24 dim(hh) # it contains all the variables besides the variables in all_h data set. [1] 640 415 require(e1071) svm.tune<-tune(svm, as.factor(out) ~ ., data=all_h, ranges=list(gamma=2^(-5:5), cost=2^(-5:5)))# find the best parameters. bestg<-svm.tune$best.parameters[[1]]

Hello, if I try to tune my svm with the code: Tune <- tune.svm(Data.Train, Class.Train, type="C-classification", kernel="radial", gamma = 2^(-1:1), cost = 2^(2:4)) i get a windows Messagebox with a error in the application "Rgui.exe" and the message: "Die Anweisung in 0x6c48174d verweist auf Speicher 0x00000000. Der Vorgang "read" konnte nicht auf

I'm running SVM from e1071 package on a data with ~150 columns (variables) and 50000 lines of data (it takes a bit of time) for radial kernel for different gamma and cost values. I get a very large models with at least 30000 vectors and the prediction I get is not the best one. What does it mean and what could I do to ameliorate my model ? Jerzy Orlowski

In e1071 package/svm default epsilon value is set to 0.1 and not 0.5 as documentation says. R

Hi I am trying to fit an svm to predict speech recognition errors. I am using best.svm like this: svm.model = best.svm(data[1:3000,1:23],data[1:3000,24],tunecontrol = tune.control()) I got this: > print(svm.model) Call: best.svm(x = data[1:3000, 1:23], tunecontrol = tune.control(), data[1:3000, 24]) Parameters: SVM-Type: eps-regression SVM-Kernel: radial cost: 1

Hi All, I'm attempting to use eps-regression or nu-regression SVM to compute probabilities but the predict function applied to an svm model object returns values outside [0, 1]: Variable Data looks like: Present X02 X03 X05 X06 X07 X13 X14 X15 X18 1 0 1634 48 2245.469 -1122.0750 3367.544 11105.013 2017.306 40 23227 2 0 1402 40 2611.519 -811.2500 3422.769 10499.425 1800.475 40 13822 3 0 1379

Hi I am doing this sort of thing: POLY: > > obj = best.tune(svm, similarity ~., data = training, kernel = "polynomial") > summary(obj) Call: best.tune(svm, similarity ~ ., data = training, kernel = "polynomial") Parameters: SVM-Type: eps-regression SVM-Kernel: polynomial cost: 1 degree: 3 gamma: 0.04545455 coef.0: 0

Dear all, I'm not able to solve easily the following simple problem. I really hope someone can give me some hints. I trained an svm (e1071). Now I'd like to show the results graphically. I used plot.svm and I'd like to add some other objects to the plot: points, (coloured) ellipses to indicate some intersting regions, curves, and so on... I tried to pass these as additional

Dear All, I've a question about scaling the input variables for an analysis with svm (package e1071). Most of my variables are factors with 4 to 6 levels but there are also some numeric variables. I'm not familiar with the math behind svms, so my assumtions maybe completely wrong ... or obvious. Will the svm automatically expand the factors into a binary matrix? If I add numeric

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

Hello, I'm using the e1071 package for training an SVM. It seems to be working well. This question has two parts: 1) Once I've trained an SVM model, I want to USE it within R at a later date to predict various new data. I see the write.svm command, but don't know how to LOAD the model back in so that I can use it tomorrow. How can I do this? 2) I would like to add the

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