Modelling a decision tree with very large data?
currently I am trying to create decision tree models with large data. The problem which occurs is, that the decision tree either gets to large (wide) or to small, so that accuracy is low and connections can't be identified. I already tried doing different things like discretize numerical attributes etc. But it won't work well. Most of the attributes are of the type nominal, just one is of the numerical type. Contrary to the titanic-example I don't have a label with "yes/no". I already thought that this may cause the problem?
Thank you for your help!