  In high-energy physics, with the search for ever smaller signals in ever
larger data sets, it has become essential to extract a maximum of the available
information from the data. Multivariate classification methods based on machine
learning techniques have become a fundamental ingredient to most analyses. Also
the multivariate classifiers themselves have significantly evolved in recent
years. Statisticians have found new ways to tune and to combine classifiers to
further gain in performance. Integrated into the analysis framework ROOT, TMVA
is a toolkit which hosts a large variety of multivariate classification
algorithms. Training, testing, performance evaluation and application of all
available classifiers is carried out simultaneously via user-friendly
interfaces. With version 4, TMVA has been extended to multivariate regression
of a real-valued target vector. Regression is invoked through the same user
interfaces as classification. TMVA 4 also features more flexible data handling
allowing one to arbitrarily form combined MVA methods. A generalised boosting
method is the first realisation benefiting from the new framework.
