randomForestVIP: Tune Random Forests Based on Variable Importance & Plot Results
Functions for assessing variable relations and associations
prior to modeling with a Random Forest algorithm (although these are
relevant for any predictive model).
Metrics such as partial correlations and variance inflation factors
are tabulated as well as plotted for the user. A function is available
for tuning the main Random Forest hyper-parameter based on model performance
and variable importance metrics. This grid-search technique provides
tables and plots showing the effect of the main hyper-parameter on each
of the assessment metrics. It also returns each of the evaluated models
to the user. The package also provides superior variable importance plots
for individual models. All of the plots are developed so that the
user has the ability to edit and improve further upon the
plots. Derivations and methodology are described in Bladen (2022)
<https://digitalcommons.usu.edu/etd/8587/>.
| Version: |
0.1.3 |
| Depends: |
R (≥ 4.0.0) |
| Imports: |
car, dplyr, ggplot2, gridExtra, minerva, randomForest, stats, tidyr |
| Suggests: |
EZtune, e1071, knitr, MASS, rmarkdown, rpart, testthat (≥
3.0.0) |
| Published: |
2023-07-19 |
| Author: |
Kelvyn Bladen [aut, cre],
D. Richard Cutler [aut] |
| Maintainer: |
Kelvyn Bladen <kelvyn.bladen at usu.edu> |
| License: |
GPL-3 |
| URL: |
https://github.com/KelvynBladen/randomForestVIP |
| NeedsCompilation: |
no |
| Materials: |
README |
| CRAN checks: |
randomForestVIP results |
Documentation:
Downloads:
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