Hello,
I am trying to use SVM from e1071 package for doing binary classification
and I am having problems in prediction using SVM. I ran a SVM for
X~as.factor(X1)+as.factor(X2)+as.factor(X3),data=data1,cross=10.
str(data1) gives me
data.frame': 5040 obs. of 5 variables:
$ X4: int 1 2 3 4 5 6 7 8 9 10 ...
$ X : int 0 1 0 1 0 0 1 1 1 1 ...
$ X2: int 1 8 15 18 1 14 10 9 8 8 ...
$ X3 : int 9 9 13 13 9 1 1 9 9 9 ...
$ X1 : num 29105 29105 29105 29105 29105 ...
As factors, X has 2 levels 0,1
X1 has 3 levels 29104.91,29526.78,10401.78
X2 has 19 levels and
X3 has 24 levels
The test data details are as follows:
str(test)
data.frame': 6267 obs. of 4 variables:
$ X4: int 1 2 3 4 5 6 7 8 9 10 ...
$ X2 : int 10 1 18 18 18 5 14 8 8 16 ...
$ X3: int 25 25 27 33 10 10 17 17 33 6 ...
$ X1 : num 29105 29105 29105 29105 29105 ..
I want the prediction of X consolidated on the basis of X4 into a separate
data frame. The loop that I wrote for this is:
len<-nrow(test);
prediction<-data.frame(t(rep(NA,2)));
names(prediction)<-c("X4","PredictionX");
prediction<-prediction[-1,];
for(j in 1:len)
{
prediction[j,1]<-test[j,1];
Forecast.List[j,2]<-predict(X,test[j,]);
}
when I do this I get the following error: Error in `contrasts<-`(`*tmp*`,
value = "contr.treatment") :
contrasts can be applied only to factors with 2 or more levels.
However, when I simply say predict(X,test), it works. When in the loop it
throws the above error. In the test data, I didn't declare the X1,X2,X3 to
be factors. Is it a factor problem? Where am I going wrong here?
Also, I want the svm predict function to not omit the NA's and keep the
missing value predictions as NA and give the prediction for the rest. In
short I do not want the function to omit the missing values from the
prediction data frame. How to resolve this?
Thanks
Divya
--
View this message in context:
http://r.789695.n4.nabble.com/Help-in-SVM-prediction-tp3810256p3810256.html
Sent from the R help mailing list archive at Nabble.com.
