  High Energy Physics data sets are often characterized by a huge number of
events. Therefore, it is extremely important to use statistical packages able
to efficiently analyze these unprecedented amounts of data. We compare the
performance of the statistical packages StatPatternRecognition (SPR) and
Toolkit for MultiVariate Analysis (TMVA). We focus on how CPU time and memory
usage of the learning process scale versus data set size. As classifiers, we
consider Random Forests, Boosted Decision Trees and Neural Networks. For our
tests, we employ a data set widely used in the machine learning community,
"Threenorm" data set, as well as data tailored for testing various edge cases.
For each data set, we constantly increase its size and check CPU time and
memory needed to build the classifiers implemented in SPR and TMVA. We show
that SPR is often significantly faster and consumes significantly less memory.
For example, the SPR implementation of Random Forest is by an order of
magnitude faster and consumes an order of magnitude less memory than TMVA on
Threenorm data.
