RapidMiner 9.7 is Now Available
Lots of amazing new improvements including true version control! Learn more about what's new here.
Poor recall and precision classification results
Hello RapidMiner community!
As a newbie to the machine learning and data mining world, I'd first like to extend my thanks to the RapidMiner team for working so hard on the tutorials to make the topic as accessible as possible. Your software is a joy to use. Now onto my problem.
I'm performing tool testing as part of a student assignment where I have to compare RapidMiner and Weka in both experimental results and in general. I'm having some problems currently with the experimental part of my assignment. My task is to compare three RapidMiner implementations of classification algorithms with three of Wekas. In my case this means DecisionTree vs. J48, k-NN vs. iBK and respective implementations of NaiveBayes. Parameters are default, except that I have disabled Laplace smoothing for NaiveBayes. I've used 10-fold Cross validation, using Performance (Polynominal) operator.
The accuracy of RapidMiner is fine and compares well to Weka's implementations, DecisionTree does better in most cases as a matter of fact. The recall and precision are somewhat troublesome though. Consider the following tables:
As you can see for the majority of cases, Weka has better results. I was hoping if you could enlighten me as to why. Am I doing something very wrong or is there something else afoot?