EZtune
3.1.1Tunes AdaBoost, Elastic Net, Support Vector Machines, and Gradient Boosting Machines
Overview
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.
Install
Health
- NOTE r-devel-linux-x86_64-debian-clang
- NOTE r-devel-linux-x86_64-debian-gcc
- NOTE r-devel-linux-x86_64-fedora-clang
- NOTE r-devel-linux-x86_64-fedora-gcc
- NOTE2026-06-099 OK · 4 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
- ERROR2026-06-089 OK · 3 NOTE · 0 WARNING · 1 ERROR · 0 FAILURE
- NOTE2026-03-1010 OK · 4 NOTE · 0 WARNING · 0 ERROR · 0 FAILURE
Documentation
- Examples that run
- 100%
- Documented parameters
- 100%
- Return-value docs
- 100%
- References docs
- 0%
Downloads
Dependencies
Code & Tests
Datasets
People & History
4 releases. Pick two to compare their code metrics. R releases are shown for context.
- RR 4.6.0 released · 2026-04-24
- RR 4.5.0 released · 2025-04-11
- RR 4.4.0 released · 2024-04-24
- RR 4.3.0 released · 2023-04-21
- RR 4.2.0 released · 2022-04-22
- 3.1.1Latest
- RR 4.1.0 released · 2021-05-18
- 3.0.02020-11-26 · diff ↗
- RR 4.0.0 released · 2020-04-24
- 2.0.02019-06-29 · diff ↗
- RR 3.6.0 released · 2019-04-26
- 1.0.02018-10-14
- RR 3.5.0 released · 2018-04-23
Package metadata
- First published
- 2018-10-14
- Total releases
- 4 / 8 yrs
- License
- GPL-3 OSI
- Minimum R
- ≥ 3.1.0
- Bundled data
- 1.1 MB / 4 files
- Download size
- 1.3 MB
- Installed size
- not tracked yet
- With dependencies
- not tracked yet