Tunes AdaBoost, Support Vector Machines, and Gradient Boosting Machines

Contains two functions that are intended to make tuning supervised learning methods easy. The eztune function uses a genetic algorithm or Hooke-Jeeves optimizer to find the best set of tuning parameters. The user can choose the optimizer, the learning method, and if optimization will be based on accuracy obtained through validation error, cross validation, or resubstitution. The function will compute a cross validated error rate. The purpose of eztune_cv is to provide a cross validated accuracy or MSE when resubstitution or validation data are used for optimization because error measures from both approaches can be misleading.

R package EZtune for download

This R package works, but is in development. Anyone is free to use it, but all code is owned by Jill Lundell and cannot be used for purposes outside of the use of this package.


Reference manual

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2.0.0 by Jill Lundell, a year ago

Browse source code at

Authors: Jill Lundell [aut, cre]

Documentation:   PDF Manual  

GPL-3 license

Imports ada, e1071, GA, gbm, optimx, rpart

Suggests knitr, rmarkdown, mlbench, doParallel, parallel

See at CRAN