It provides ensemble capabilities to supervised and unsupervised learning models predictions without using training labels. It decides the relative weights of the different models predictions by using best models predictions as response variable and rest of the mo. User can decide the best model, therefore, It provides freedom to user to ensemble models based on their design solutions.
| Version: | 0.4.0 |
| Depends: | R (≥ 3.5.0) |
| Imports: | caret (≥ 6.0.78), dplyr, randomForest, ggplot2, rlist (≥ 0.4.6.1), glmnet, tidyverse, e1071, purrr, pROC (≥ 1.13.0), rlang (≥ 0.2.1) |
| Suggests: | testthat, knitr, rmarkdown, ClusterR |
| Published: | 2019-01-15 |
| Author: | Aviral Vijay [aut, cre], Sameer Mahajan [aut] |
| Maintainer: | Aviral Vijay <aviral.vijay at gslab.com> |
| BugReports: | https://github.com/GSLabDev/nonet/issues |
| License: | MIT + file LICENSE |
| URL: | https://open.gslab.com/nonet/ |
| NeedsCompilation: | no |
| Materials: | README |
| CRAN checks: | nonet results |
| Reference manual: | nonet.pdf |
| Vignettes: |
nonet ensemble classification with nonet plot nonet ensemble Clustering with nonet plot nonet ensemble regression with nonet plot |
| Package source: | nonet_0.4.0.tar.gz |
| Windows binaries: | r-devel: nonet_0.4.0.zip, r-release: nonet_0.4.0.zip, r-oldrel: nonet_0.4.0.zip |
| macOS binaries: | r-release: nonet_0.4.0.tgz, r-oldrel: nonet_0.4.0.tgz |
| Old sources: | nonet archive |
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