r support vector machine e1071 training not working - r

Question

I am playing around with Support Vector Machines in the R-Language. Specifically I am using the e1071 package.
As long as I follow the manual pages or the tutorial at wikibooks everythings works. But if I try to use my own datasets with those examples things aren't that good anymore.
It seems that the model creation fails for some reason. At least I am not getting the levels on the target column. Below you find the example for clarification.
Maybe someone can help me to figure out what I am doing wrong here. So here is all the code and data.
Test dataset
target,col1,col2
0,1,2
0,2,3
0,3,4
0,4,5
0,5,6
0,1,2
0,2,3
0,3,4
0,4,5
0,5,6
0,1,2
0,2,3
0,3,4
0,4,5
1,6,7
1,7,8
1,8,9
1,9,0
1,0,10
1,6,7
1,7,8
1,8,9
1,9,0
1,0,10
1,6,7
1,7,8
1,8,9
1,9,0
1,0,10
R-Script
library(e1071)
dataset <- read.csv("test.csv", header=TRUE, sep=',')
tuned <- tune.svm(target~., data = dataset, gamma = 10^(-6:-1), cost = 10^(-1:1))
summary(tuned)
model <- svm(target~., data = dataset, kernel="radial", gamma=0.001, cost=10)
summary(model)
Output of the summary(model) statement
+ summary(model)
Call:
svm(formula = target ~ ., data = dataset, kernel = "radial", gamma = 0.001,
cost = 10)
Parameters:
SVM-Type: eps-regression
SVM-Kernel: radial
cost: 10
gamma: 0.001
epsilon: 0.1
Number of Support Vectors: 28
>
Wikibooks examaple
If I compare this output to the output of the wikibooks example, it's missing some information. Please notice the "Levels"-Section in the output:
library(MASS)
library(e1071)
data(cats)
model <- svm(Sex~., data = cats)
summary(model)
Output
> summary(model)
Call:
svm(formula = Sex ~ ., data = cats)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1
gamma: 0.5
Number of Support Vectors: 84
( 39 45 )
Number of Classes: 2
Levels:
F M

Answer 1

Putting Roland's answer in the proper "answer" format:
target is numeric
sex is a factor
Let me give a few more suggestions:
it seems as if target really should be a factor. (It has only 2 levels, 0 & 1, and I suspect you're trying to classify into either 0 or 1.) So stick in a dataset$target <- factor(dataset$target) somewhere.
right now, because target is a numeric, a regression model is being run instead of a classification.
it's worthwhile to do a similar check for any of your variables before running a model (especially a model). In the case you gave, for instance, it's not obvious what col1 and col2 are. If either of them are a grouping or classification, you should also make them factors, too.
In R, many functions have multiple ways in which they will run, depending upon the data types fed to them. If you feed factors into a model, it will run classification. If you feed numerics, regression. This is actually common in many programming languages, and is called function overloading.