UBL: an R package for Utility-based Learning
This is an incremental contribution that packages existing techniques into a tool for researchers and practitioners in various fields dealing with utility-based predictive tasks.
The paper introduces the UBL R package, which provides methods for utility-based learning to handle classification and regression problems with non-uniform costs and benefits, aiming to enhance predictive performance by incorporating user preferences in domains like meteorology, finance, and medicine.
This document describes the R package UBL that allows the use of several methods for handling utility-based learning problems. Classification and regression problems that assume non-uniform costs and/or benefits pose serious challenges to predictive analytic tasks. In the context of meteorology, finance, medicine, ecology, among many other, specific domain information concerning the preference bias of the users must be taken into account to enhance the models predictive performance. To deal with this problem, a large number of techniques was proposed by the research community for both classification and regression tasks. The main goal of UBL package is to facilitate the utility-based predictive analytic task by providing a set of methods to deal with this type of problems in the R environment. It is a versatile tool that provides mechanisms to handle both regression and classification (binary and multiclass) tasks. Moreover, UBL package allows the user to specify his domain preferences, but it also provides some automatic methods that try to infer those preference bias from the domain, considering some common known settings.