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.


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install.packages("nonet")

0.4.0 by Aviral Vijay, a month ago


https://open.gslab.com/nonet/


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