swag - Sparse Wrapper Algorithm
An algorithm that trains a meta-learning procedure that
combines screening and wrapper methods to find a set of
extremely low-dimensional attribute combinations. This package
works on top of the 'caret' package and proceeds in a
forward-step manner. More specifically, it builds and tests
learners starting from very few attributes until it includes a
maximal number of attributes by increasing the number of
attributes at each step. Hence, for each fixed number of
attributes, the algorithm tests various (randomly selected)
learners and picks those with the best performance in terms of
training error. Throughout, the algorithm uses the information
coming from the best learners at the previous step to build and
test learners in the following step. In the end, it outputs a
set of strong low-dimensional learners.
