I working with R on a classification problem. My outcome variable is binary with two levels 1 and 2. First of all I tried the logistic regression, which of all methods has the best performance, altough still poor.
I tried nnet package, random forest, the fuzzy package frbs and decision trees.
The nnet function gives me only one class - in this case 2.
I had some hope with frbs package. See my code below:
obj <- frbs.learn(train,method.type="FRBCS.CHI",control=list(num.labels=3,type.mf="GAUSSIAN")) summary(obj) #test set without def pred<-predict(obj,newdata=test[,1:8])
But the predictions are wrong, the class 1 is completely missclassified
#percentage error tdef<-test$def err = 100*sum(pred!=tdef)/ nrow(pred) print(err)  16.93038
I'm wondering what I could improve to classify the output variable. Is something wrong with my data? Are the parameters not right?
Can someone please verifiy? I'm at the end of my knowledge...
You can find the (normalized) data here: https://drive.google.com/open?id=1xrCXTLqKvGiGeo2X0Y1DvoSKvzbYFnyccLimceDIbZg