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
When I specify the method ="eps-regression" is the same
svm.m1 <- svm(st ~ ., data = train, method="eps-regression", cost = 1000, gamma
= 1e-03)
Call:
svm(formula = st ~ ., data = train, method = "eps-regression", cost = 1000,
gamma = 0.001)
Parameters:
SVM-Type: C-classification
SVM-Kernel: radial
cost: 1000
gamma: 0.001
Number of Support Vectors: 209
I know that it is wrong because when I do prediction appear levels.
I'm working with normalized data [0,1] (249 points) . I don't have idea what it
is wrong.
Somebody can help me?
h_aspire
dados=read.table("svmdata.txt",header=TRUE)
index=1:nrow(d)
test=d[210:249,]
train=d[1:209,]
require(e1071)
tuneobj = tune.svm(st ~ ., data = train, gamma = 10^(-6:-3), cost =
10^(1:3summary(tuneobj)
svm.m1 <- svm(st ~ ., data = train, cost = 1000, gamma = 1e-03)
svm.pred <- predict(svm.m1, test)
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