Compete in RapidMiner's 3rd Competition: Fantasy Football. Top prize is $750. Deadline December 19.
Download RapidMiner Studio or Server 8.0 Public Beta. Let us know how you like it! Ends November 27.
Watch RapidMiner's "Getting Started" videos on YouTube. Everything you need to do data science - fast and simple!
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!
Solved! Go to Solution.
Thank you for your reply Thomas!
Yes, I played with all three parameter (confidence, minimal leafsize, minimal leafsize of split) but I can't come up with something useable or "easy to read" like the titanic example did.
Did you change your Tree Depth parameter? The default is 20 which is pretty big, I just usually set it to 5.
Both the Min Leaf and Min Leaf to Split are pretty important as Pre-pruning parameters. I would try bumping those values up to something larger than you have now.
A few additional thoughts:
i support Brian's arguments. Decision Trees are great tool to start with and to still keep an understanding of your model. But i think you run into the limitations of what you can do with a tree. Just think the limitations of how a single tree of depth 5 can cut into your hyperspace. This can not a very detailed classification.
I would recommend to try a random forest and later a gbt. You loose interpretability but get prediction performance.
Thank you all for your help!
I integrated all your optimizations into my process. To make the tree more "readable" I set the prepruning parameters different (minimal gain 0.01 and moreover I set the general confidence up to 0.25). Moreover since my label consisted of nearly twenty different names I tried to classify them into two groups with I think had the biggest impact on my tree. Positively, the accuracy didn't decrease. The contrary happened, it increased (x-Validation 82 %).
So to put it briefly in a nutshell I have a tree I can work with!
Thank you again for your answers!