The optimization plugin for RapidMiner aims to introduce mathematical optimization based learning capability to RapidMiner. An unconstrained optimization problem, which is called learning (or) training in machine learning can be solved with different algorithms. Each algorithm has a set of configurable items, that are mainly function to be optimized, choice of search direction, regularization type,choice of learning rate (line search) . There are different options or ways available to perform the task pertaining to each configuration. This plugin aims to give a clean separation of these configurable items, so that the user can configure these items before starting the learning. Thus, giving the user, an easily configurable optimization based learner.
A technical report describing the optimization plugin can be found in our tech report list.