By sampling your data, running the machine learning 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 machine learning algorithm and possibly even more accurate predictions.
This package consists of two main functions:
parallelML A function which allows you to create multiple machine learning models: one for each core you provide. It returns a list of machine learning models.
predictML: A function which creates predictions for all models you created. When combine is "vote", it returns the class most models agree upon, otherwise it gives a list of predictions of all models
Maintainer: Wannes Rosiers <firstname.lastname@example.org>
This package can be regarded as a parallel extension of machine learning algorithms, therefor check the package of the machine learning algorithm you want to use.
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## Not run: # Load the library which provides svm library(e1071) # Create your data data(iris) # Create a model parSvmModel <- parallelML("svm(formula = Species ~ ., data = iris)", "e1071",samplingSize = 0.8) # Get prediction parSvmPred <- predictML("predict(parSvmModel,newdata=iris)", "e1071","vote") # Check the quality table(parSvmPred,iris$Species) ## End(Not run)
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