First of all, sorry about my english, I am brazilian and I am improving it yet.
I have a hierarchical dataset which I used to use to create flat classification models (NaiveBayes, JRip, J48, SVM)... For example:
> model<-svm(family~.,data=train)
> pred <-predict(model, test[,-ncol(test)])
And then I calculated Precision, Recall and F-measure, ignoring the fact that the dataset is organized hierarchically.
However, now I want to explore the fact that it is hierarchical and obtain different models and results. So what should I do? Considering the same ML algorithms (NaiveBayes, JRip, J48, SVM), how do I create the models? Should I change or include new parameters? Or should I continue as shown in the code before, and just use hierarchical Precision, hierarchical Recall and hierarchical F-measure as evaluation metrics? If so, is there any specific package?
Thanks!