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R | ||
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.. include:: /global.rst | ||
.. highlight:: r | ||
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In this page we briefly discuss the R wrapper, which provides most of Optunity's functionality. | ||
For a general overview, we recommend reading the :doc:`/user/index`. | ||
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For installation instructions, please refer to |installation|. To use the package has to be loaded like all R packages by:: | ||
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library(optunity) | ||
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Manual | ||
-------- | ||
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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. | ||
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For R following main functions are available: | ||
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- `cv.setup` for **setting up CV**, specifying data and the number of folds | ||
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- `cv.particle_swarm`, `cv.nelder_mead`, `cv.grid_search`, `cv.random_search` for **optimizing hyperparameters** in CV setting. | ||
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- `auc_roc` and `auc_pr` for calculating **AUC** of ROC and precision-recall curve. | ||
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- `early_rie` and `early_bedroc` for **early discovery** metrics. | ||
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- `mean_se` and `mean_ae` for regression **loss functions**, mean squared error and mean absolute error. | ||
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- `particle_swarm`, `nelder_mead`, `grid_search`, `random_search` for minimizing (or maximizing) regular functionals. | ||
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- `cv.run` performs a **cross-validation** evaluation for the given values of the hyperparameters (no optimization). | ||
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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. | ||
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Examples | ||
---------- | ||
Please see the example pages, | ||
:doc:`/examples/r/ridgereg` and :doc:`/examples/r/svm`. The R help pages for each function | ||
contain examples showing how to use them. | ||
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