  Training features used to analyse physical processes are often highly
correlated and determining which ones are most important for the classification
is a non-trivial tasks. For the use case of a search for a top-quark pair
produced in association with a Higgs boson decaying to bottom-quarks at the
LHC, we compare feature ranking methods for a classification BDT. Ranking
methods, such as the BDT Selection Frequency commonly used in High Energy
Physics and the Permutational Performance, are compared with the
computationally expense Iterative Addition and Iterative Removal procedures,
while the latter was found to be the most performant.
