Parallel-Voting version of machine learning algorithms

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Description

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.

Details

Package: parallelML
Type: Package
Version: 1.0
Date: 2015-09-06
License: GPL-2

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

Author(s)

Wannes Rosiers

Maintainer: Wannes Rosiers <wannes.rosiers@infofarm.be>

See Also

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.

Examples

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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)