ModelApplier needs to much memory with high-dimensional data?
I was playing around with the cross validation for some time using one of the templates that come with RapidMiner and the sparse toy data file. Using the toy data, the standard-XVal with a LibSVM classification learner + ModelApplier + Evauator runs in less than 2 sek.
Then I changed the the dimension of the data from the current 25 features to something larger (e.g. 100000), simply by adding 1 additional feature with the index 99999 and some value to each of my 10 sparse data vectors.
Unfortunately, the application (!) of the learned model to the test data now runs extremely long, using incredible amounts of memory. When I do the same without RapidMiner, using a simple perl script and the standard LibSVM implementation, the XVal is again done in seconds. Am I using the wrong ModelApplier or wrong options?
Thank you so much,