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

arXiv: https://arxiv.org/abs/1412.0436 · HTML: https://mlsanity.com/paper/1412.0436/

## Key facts

- Cited by 4 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 1 Dec 2014.

## Built on by (most cited first, 4 of 4)

- [A Survey on Spatio-temporal Data Analytics Systems](https://mlsanity.com/paper/2103.09883/) (2021)
- [Beyond Average Performance -- exploring regions of deviating performance for black box classification models](https://mlsanity.com/paper/2109.08216/) (2021)
- [EEG Signal Processing using Wavelets for Accurate Seizure Detection through Cost Sensitive Data Mining](https://mlsanity.com/paper/2109.13818/) (2021)
- [Seizure detection from Electroencephalogram signals via Wavelets and Graph Theory metrics](https://mlsanity.com/paper/2312.00811/) (2023)

## Abstract (arXiv)

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.