By sampling your data, running the Support-Vector-Machine algorithm on these samples in parallel on your own machine and letting your models vote on a prediction, we return much faster predictions than the regular Support-Vector-Machine and possibly even more accurate predictions.
This package consists of two main functions:
parallelSVM A function which allows you to create multiple Support-Vector-Machine models: one for each core you provide. It returns a list of Support-Vector-Machine models.
predict: An extension of the predict function, which uses the prediction of each Support-Vector-Machine model. When probability is TRUE, it returns the average of all predictions, otherwise it returns the class most models agree upon.
Maintainer: Wannes Rosiers <firstname.lastname@example.org>
This package can be regarded as a parallel extension of
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