Displaying 7 results from an estimated 7 matches for "svmpred".
2011 Feb 02
2
SVM Prediction and Plot
Hi
I'm trying to predict using a model I fitted with SVM.
I constructed the model (called Svm) using a training set, and now I want to
use a test set (called BankTest) for prediction.
The response variable is in the first column of BankTest.
> SvmPred = predict(Svm, BankTest[,-1], probability=TRUE)
> SvmPredRes = table(Pred = SvmPred, True = BankTest[,1])
I get this error:
Error in table(Pred = SvmPred, True = BankTest) : all arguments must have
the same length
I checked the length of both BankTest[,1] and SvmPredict.
> length(SvmPred)...
2011 Feb 22
0
why no "probabilities" from svm.predict?
> library(ROCR)
> library(e1071)
svmres.prob <- svm(traindx, traindy, probability=TRUE)
svmpred.prob <- predict(svmres.prob, testdx, probability = TRUE,
decision.values = TRUE, type="prob")
> print(length(attr(svmpred.prob, "probabilities")))
[1] 0
> print(attr(svmpred.prob, "probabilities"))
NULL
> print(attributes(svmpred.prob)$decision.values)...
2011 Feb 21
3
ROC from R-SVM?
*Hi,
*Does anyone know how can I show an *ROC curve for R-SVM*? I understand in
R-SVM we are not optimizing over SVM cost parameter. Any example ROC for
R-SVM code or guidance can be really useful.
Thanks, Angel.
[[alternative HTML version deleted]]
2013 Apr 14
1
Aggregate function Bagging
...s = NULL, bagControl = bagControl(), ...)
bagControl(fit = NULL,
predict = NULL,
aggregate = NULL,
downSample = FALSE)
My fit function is:
svmFit <- function(x, y, ...)
{
library(e1071)
svm(Score~., data = mydataset)
}
My predict function is :
svmPred <- function(object, x)
{
predict(object, x)[,1]
}
However, I don't know how to build the aggregate function.
Does anyone know how to develop it?
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2012 Dec 10
3
splitting dataset based on variable and re-combining
I have a dataset and I wish to use two different models to predict. Both models are SVM. The reason for two different models is based
on the sex of the observation. I wish to be able to make predictions and have the results be in the same order as my original dataset. To
illustrate I will use iris:
# Take Iris and create a dataframe of just two Species, setosa and versicolor, shuffle them
2007 Jan 22
0
Recursive-SVM (R-SVM)
...er) )
{
## record the genes selected in this ladder
SelFreq[SelInd, gLevel] <- SelFreq[SelInd, gLevel] +1
## train SVM model and test error
svmres <- svm(xTrain[, SelInd], yTrain, scale=F, type="C-classification", kernel="linear" )
if( CVtype == "LOO" )
{
svmpred <- predict(svmres, matrix(xTest[SelInd], nrow=1) )
} else
{
svmpred <- predict(svmres, xTest[, SelInd] )
}
ErrVec[gLevel] <- ErrVec[gLevel] + sum(svmpred != yTest )
## weight vector
W <- t(svmres$coefs*yTrain[svmres$index]) %*% svmres$SV * md[SelInd]
rkW <- rank(W)
if( gLeve...
2002 Aug 20
0
Re: SVM questions
...).
Now, ``libsvm'' actually returns a_i * y_i as i-th coefficiant and the
*negative* rho, so in fact uses the formula:
Sum(coef_i * K(x_i, n)) - rho
i
where the training examples (=training data) are labeled {1,-1}.
A simplified R function for prediction with linear kernel would be:
svmpred <- function (m, newdata, K=crossprod) {
## this guy does the computation:
pred.one <- function (x)
sign(sum(sapply(1:m$tot.nSV, function (j)
K(m$SV[j,], x) * m$coefs[j]
)
) - m$rho
)
## this is just for convenience:
if...