Machine learning problem - train and test with different classes

[Inuraghe]

I have a datasets composed like this:

library(caret)

data <- iris
train <- data [1:75,]
test <- data[76:150,]

So, I have 3 classes in total but:

1. Train: setosa and versicolor
2. Test: versicolor and virginica

I tryed to set a decision tree, but obviusly it doesn't work because train and test haven't the same classes. My R code is:

trainctrl <- trainControl(method = "cv", number = 5, verboseIter = FALSE)
dt.model <- train(Species~., data=train, method = "rpart", 
                  tuneLength = 10,
                  preProcess = c("center", "scale"),
                  trControl = trainctrl)

test$prediction <-predict(dt.model, test)

Is there a model that if in the test meets a virginica class flower is able to tell me that it is another species? In other words, I want a model that can tell me that that flower is completely different from the ones he trained on. Is it possible?

[ttrodrigz]

How would the model be able to tell if an observation is completely different if it never saw an example of a completely different observation? And, how different would an observation have to be to be considered "completely different?" This would have to be decided quantitatively somehow and it isn't something that a model is going to be able to learn unless you provide it the data.