thomasWeise/learnerSelectoR: An R Package for Automatically Selecting and Applying a (Machine) Learning Method

This package provides an infrastructure for selecting the right Machine Learning method to process a given dataset automatically and then also applies it. This package thus is something which will likely be used under-the-hood by our other packages to come. The goal is to make a better, more robust choice regarding which Machine Learning method to use than just applying all to the complete data and picking the best results. However, we here are still at a very early stage in this development. This package is intented to work together with other packages for model fitting or regression, classification, and clustering. The goal is to provide an abstract method to select the right model or learning algorithm: The main method, \code{\link{learning.learn}} is provided with the input data and the size of the input data. It does not need to know the exact nature of the data, though. It is further provided with functions that can be used to partition the data based on a selection of sample indices. This way, it can internally realize cross-validation. The method also is provided a set of learning methods (or models) in form of a list of functions. After choosing the learning method or model which generalizes better than the others and produces models of small size, this method is then applied to the complete dataset and its result is returned. The learning method can also receive the input data in different representations: Let's say you want to fit a model to some x-y data. Then you can do that both on the original data as well as on log-scaled data. (Testing is always done on the original data). This way you have two possible realizations of the same model. The one which generalizes better is chosen.

install.packages("remotes")
remotes::install_github("thomasWeise/learnerSelectoR")