provides this infrastructure so that you can focus on your experiments! The framework provides supervised methods like classification, regression and survival analysis along with their corresponding evaluation and optimization methods, as well as unsupervised methods like clustering.
This often forces users to make crummy trade-offs in their experiments due to time constraints or lacking expert programming skills. Therefore, for any non-trivial experiments, you need to write lengthy, tedious and error-prone wrappers to call the different algorithms and unify their respective output.Īdditionally you need to implement infrastructure to R does not define a standardized interface for its machine-learning algorithms.
