Package: EZtune 3.1.2

EZtune: Tunes AdaBoost, Elastic Net, 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 eztune_cv 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.

Authors:Jill Lundell [aut, cre]

EZtune_3.1.2.tar.gz
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EZtune_3.1.2.tgz(r-4.4-any)EZtune_3.1.2.tgz(r-4.3-any)
EZtune_3.1.2.tar.gz(r-4.5-noble)EZtune_3.1.2.tar.gz(r-4.4-noble)
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EZtune.pdf |EZtune.html✨
EZtune/json (API)

Peer review:

Bug tracker:https://github.com/jillbo1000/eztune/issues

On CRAN:

2 exports 0.93 score 59 dependencies 1 dependents 38 scripts 267 downloads

Last updated 3 years agofrom:6fe00c1d0d. Checks:OK: 1 NOTE: 6. Indexed: yes.

Exports:eztuneeztune_cv

Dependencies:adabase64encBiocManagerBiocStylebitopsbookdownbslibcachemcaToolsclassclicodetoolscrayondigeste1071evaluatefastmapfontawesomeforeachfsGAgbmglmnetgluegplotsgtoolshighrhtmltoolsiteratorsjquerylibjsonliteKernSmoothknitrlatticelifecycleMASSMatrixmemoisemimenloptrnumDerivoptimxpracmaproxyR6rappdirsRcppRcppArmadilloRcppEigenrlangrmarkdownROCRrpartsassshapesurvivaltinytexxfunyaml