An Infra-Structure for Performance Estimation and Experimental Comparison of Predictive Models in R

TL;DR

This paper introduces an R package infrastructure that simplifies the process of estimating and comparing the predictive performance of various models across classification, regression, and time series tasks.

Contribution

The paper presents a flexible, generic R package that streamlines performance estimation and includes standard workflows for diverse predictive modeling tasks.

Findings

Supports any performance metric for different workflows

Enables easy setup of experiments with minimal input

Facilitates comprehensive model comparison in R

Abstract

This document describes an infra-structure provided by the R package performanceEstimation that allows to estimate the predictive performance of different approaches (workflows) to predictive tasks. The infra-structure is generic in the sense that it can be used to estimate the values of any performance metrics, for any workflow on different predictive tasks, namely, classification, regression and time series tasks. The package also includes several standard workflows that allow users to easily set up their experiments limiting the amount of work and information they need to provide. The overall goal of the infra-structure provided by our package is to facilitate the task of estimating the predictive performance of different modeling approaches to predictive tasks in the R environment.