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 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 |
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