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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 |
| DOI: | 10.32614/CRAN.package.performanceEstimation |
| Author: | Luis Torgo [aut, cre] |
| Maintainer: | Luis Torgo <ltorgo at dcc.fc.up.pt> |
| BugReports: | https://github.com/ltorgo/performanceEstimation/issues |
| License: | GPL-2 | GPL-3 [expanded from: GPL (≥ 2)] |
| URL: | https://github.com/ltorgo/performanceEstimation |
| 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: performanceEstimation_1.1.0.zip, r-release: performanceEstimation_1.1.0.zip, r-oldrel: performanceEstimation_1.1.0.zip |
| macOS binaries: | r-release (arm64): performanceEstimation_1.1.0.tgz, r-oldrel (arm64): performanceEstimation_1.1.0.tgz, r-release (x86_64): performanceEstimation_1.1.0.tgz, r-oldrel (x86_64): performanceEstimation_1.1.0.tgz |
| Old sources: | performanceEstimation archive |
| Reverse imports: | MSclassifR |
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These binaries (installable software) and packages are in development.
They may not be fully stable and should be used with caution. We make no claims about them.