UBL: an R package for Utility-based Learning

TL;DR

The UBL R package offers versatile tools for utility-based classification and regression, enabling domain-specific preference handling to improve predictive performance in non-uniform cost-benefit scenarios.

Contribution

This paper introduces the UBL package, a comprehensive R toolkit that facilitates utility-based learning for classification and regression with user-specified or inferred preferences.

Findings

Provides methods for handling non-uniform costs and benefits.

Supports both classification and regression tasks.

Includes automatic preference inference mechanisms.

Abstract

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.