performanceEstimation: An Infra-Structure for Performance Estimation of Predictive Models

An infra-structure for estimating the predictive performance of predictive models. In this context, it can also be used to compare and/or select among different alternative ways of solving one or more predictive tasks. The main goal of the package is to provide a generic infra-structure to estimate the values of different metrics of predictive performance using different estimation procedures. These estimation tasks can be applied to any solutions (workflows) to the predictive tasks. The package provides easy to use standard workflows that allow the usage of any available R modeling algorithm together with some pre-defined data pre-processing steps and also prediction post- processing methods. It also provides means for addressing issues related with the statistical significance of the observed differences.

Version: 1.1.0
Depends: R (≥ 3.0), methods
Imports: ggplot2 (≥ 0.9.3), parallelMap (≥ 1.3), parallel, tidyr (≥ 0.4.1), dplyr (≥ 0.4.3)
Suggests: knitr, rmarkdown, devtools, e1071, DMwR, randomForest, quantmod, nnet, mlbench, MASS
Published: 2016-10-13
Author: Luis Torgo [aut, cre]
Maintainer: Luis Torgo <ltorgo at>
License: GPL-2 | GPL-3 [expanded from: GPL (≥ 2)]
NeedsCompilation: no
Citation: performanceEstimation citation info
CRAN checks: performanceEstimation results


Reference manual: performanceEstimation.pdf
Package source: performanceEstimation_1.1.0.tar.gz
Windows binaries: r-devel:, r-release:, r-oldrel:
OS X El Capitan binaries: r-release: performanceEstimation_1.1.0.tgz
OS X Mavericks binaries: r-oldrel: performanceEstimation_1.1.0.tgz
Old sources: performanceEstimation archive


Please use the canonical form to link to this page.