parallelML-package: Parallel-Voting version of machine learning algorithms

Description Details Author(s) See Also Examples

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 <[email protected]>

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)

parallelML documentation built on May 29, 2017, 3:59 p.m.