The Whirlwind that was RCOMM - Part 1
Incorporating and expanding on my first RCOMM 2010 post, I going to write about the various presentations that I found highly interesting and applicable to financial data mining. I walked in on Milan Vukiecevic, who gave a talk about an upcoming plugin release called Whi Bo. Unfortunately I walked in toward the end of the talk and only caught the Q&A part. Still, I was able to catch up with on the last day of RCOMM to discuss his application. Ingo from Rapid-I describes it best, it ’s like a mini Rapidminer inside Rapidminer! Essentially Whi Bo works within the Decision Tree modelers and helps the user fine tune the splitting parameters. It also enhances the modelers by detecting better splitting algorithms for your particular data set.
Right after Milan ’s talk we had another great talk about Landmarking for Meta Learning by Sarah Abdelmessih. This talk was considered a continuation of the PaREN talk I missed early in the day about pattern recognition. I found Sarah ’s discussion on determining the right learner for your particular data set to be very useful. Why? Often my readers ask me, would an SVM learner better to use in this data set? Or is Knn better? Often it ’s a combination of learners, not just one, that gives you the better answer! The end result is the creation of ranking system of learners for a given data set! I can ’t wait for the PaREN plugin to come out. Man, so many cool things were going on in those few short hours!
We closed out the day with a workshop by Tobias Malberct, Rapid-I team member about using the Reporting operators in Rapidminer and the now famous €œWho wants to be Dataminer € game show. I think the game show was the funniest thing I saw in a long time! Contestants pitted themselves against veteran Rapid-I developers with the surprise of the evening coming at the end. Contestant Matko BoÅ¡njak, from Croatia, finished surprisingly strong after only €œpicking up € Rapidminer 3 months ago. Not even the veteran Rapid-I guys could finish in the 5 minutes time given and Matko took home the prize. I believe he said that he learned how to use Rapidminer from watching my tutorials.
Dinner followed at nice local establishment only a few 100 meters from the University. We ate, drank, and chatted the night away. I met up with Milan, Matko, Ralf Klinkenberg, Ingo & Nadja Mierswa, Markus Hoffman, and Miran Matjis. Miran was presenting the next day about load forecasting electrical demand using SVMs. Although our talks were different in subject, we both applied the time series forecasting plugin for Rapidminer and had LOTS to talk about that night and the next, but I ’ll leave those adventures for tomorrow.Don't forget to sign up for our monthly newsletter on Data Science and RapidMiner here!