In this page we briefly discuss the R wrapper, which provides most of Optunity’s functionality. For a general overview, we recommend reading the User Guide.
For installation instructions, please refer to Installing Optunity. To use the package has to be loaded like all R packages by:
All functions in R wrapper have documentation available in R’s help system, e.g., ?cv.particle_swarm gives help for cross-validation (CV) using particle swarms. To see the list of available functions type optunity::<TAB><TAB> in R’s command line.
For R following main functions are available:
- cv.setup for setting up CV, specifying data and the number of folds
- cv.particle_swarm, cv.nelder_mead, cv.grid_search, cv.random_search for optimizing hyperparameters in CV setting.
- auc_roc and auc_pr for calculating AUC of ROC and precision-recall curve.
- early_rie and early_bedroc for early discovery metrics.
- mean_se and mean_ae for regression loss functions, mean squared error and mean absolute error.
- particle_swarm, nelder_mead, grid_search, random_search for minimizing (or maximizing) regular functionals.
- cv.run performs a cross-validation evaluation for the given values of the hyperparameters (no optimization).
General workflow is to first create a CV object using cv.setup and then use it to run CV, by using functions like cv.particle_swarms, cv.random_search etc.
Please see the example pages, Ridge Regression and SVM (e1071). The R help pages for each function contain examples showing how to use them.