nonet: Weighted Average Ensemble without Training Labels

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)
Published: 2019年01月15日
Author: Aviral Vijay [aut, cre], Sameer Mahajan [aut]
Maintainer: Aviral Vijay <aviral.vijay at gslab.com>
License: MIT + file LICENSE
NeedsCompilation: no
Materials: README
CRAN checks: nonet results

Documentation:

Reference manual: nonet.html , nonet.pdf

Downloads:

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 (arm64): nonet_0.4.0.tgz, r-oldrel (arm64): nonet_0.4.0.tgz, r-release (x86_64): nonet_0.4.0.tgz, r-oldrel (x86_64): nonet_0.4.0.tgz
Old sources: nonet archive

Linking:

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