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.


Reference manual

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0.4.0 by Aviral Vijay, a month ago


Report a bug at https://github.com/GSLabDev/nonet/issues

Browse source code at https://github.com/cran/nonet

Authors: Aviral Vijay [aut, cre] , Sameer Mahajan [aut]

Documentation:   PDF Manual  

MIT + file LICENSE license

Imports caret, dplyr, randomForest, ggplot2, rlist, glmnet, tidyverse, e1071, purrr, pROC, rlang

Suggests testthat, knitr, rmarkdown, ClusterR

See at CRAN