similar to: help loading an svm model from file

Hello all, I am trying to use svm (from the e1071 package) to solve a binary classification problem. The two classes in my particular data set are unequally populated. class 'I' (for important) has about 3000 instances while class "B" (for background) has about 20,000. experimenting with different classifiers I realized that in cases where such an asymmetry exists there is a

I have a data set with about 30,000 training cases and 103 variable. I've trained an SVM (using the e1071 package) for a binary classifier {0,1}. The accuracy isn't great. I used a grid search over the C and G parameters with an RBF kernel to find the best settings. I remember that for least squares, R has a nice stepwise function that will try combining subsets of variables to find

Dear Community! I'm using the svm method of package e1071 for classifying my data. This really works fine, but however I have to work after creating the support vectors and the parameters with unscaled data. So the problem is when I try to train the classifier with the option "scale=F" the result is quite poor, so training with scaled data is essential. The rescaling of the support

Dear community, I'm performing svm-regression with svm at library e1071. As I wrote in another post: "svm e1071 call - different results", I get different results if I use the svm.default rather than the svm.formula, being better the ones at svm.formula I've debugged both options. While debugging the svm.formula, I've seen that when I reach the call: ret <-

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)

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

Hello all, this is the first time I'm using R and e1071 package and SVM multiclass (and I'm not a statistician)! I'm very confused, then. The goal is: I have a sentence with sunny; it will be classified as "yes" sentence; I have a sentence with cloud, it will be classified as "maybe"; I have a sentence with rainy il will be classified as "no". The

Hi, If I try to read the codes of functions in e1071 package, it gives me following error message. >library(e1071) > svm function (x, ...) UseMethod("svm") <environment: namespace:e1071> > predict.svm Error: Object "predict.svm" not found > Can someone help me on this how to read the codes of the functions in the e1071 package? Thanks. Raj

> install.packages("e1071") Warning: package 'e1071' is in use and will not be installed > library(e1071) > library(MASS) > p5 <- read.csv("http://www.public.iastate.edu/~aiminy/data/p_5_2.csv") > attach(p5) The following object(s) are masked from p5 ( position 3 ) : Aa As Cur Ms P Y The following object(s) are

Hi R users, I'm trying to run a SVM - regression using e1071 package but the function svm() all the time apply a classification method rather than a regression. svm.m1 <- svm(st ~ ., data = train, cost = 1000, gamma = 1e-03) Parameters: SVM-Type: C-classification SVM-Kernel: radial cost: 1000 gamma: 0.001 Number of Support Vectors: 209

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

Hello all, I am using the e1071 SVM with the tune options for classification, which work pretty well, given the examples of using tune.svm function for classification. But I have not found any example to tune the SVM novelty detection (one-classification) parameters (gamma, cost, nu), for example this are some of the options I have tried with no success: obj<-tune(svm, x,y, type

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

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'm trying to plot a pair of intertwined spirals and an svm that separates them. I'm having some trouble. Here's what I tried. > library(mlbench) > library(e1071) Loading required package: class > raw <- mlbench.spirals(200,2) > spiral <- data.frame(class=as.factor(raw$classes), x=raw$x[,1], y=raw$x[,2]) > m <- svm(class~., data=spiral) > plot(m,

Dear All: Just fixed where is the problem I am trying to use the R function "svm" with "type~." , but I got the following error message SVM.Model1 <- svm(type ~ ., data=my.data.x1x2y, *type='C-classification'*, kernel='linear',scale=FALSE) *Error in eval(predvars, data, env) : object 'type' not found* I am wondering if I should install a

Hi R, I need to see the inner code behind the function "svm" in the package e1071. I enter svm in the console and get the below output. > svm function (x, ...) UseMethod("svm") <environment: namespace:e1071> Is there any way I can look into the code of what svm (support vector machine) is doing? Thanks a lot for your help... Thanks and Regards,

Dear All: I am trying to use the R function "svm" with "type =C-classification" , but I got the following error message SVM.Model1 <- svm(type ~ ., data=my.data.x1x2y, *type='C-classification'*, kernel='linear',scale=FALSE) *Error in eval(predvars, data, env) : object 'type' not found* I am wondering if I should install a specific R