Description Details Author(s) See Also Examples
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.
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
Wannes Rosiers
Maintainer: Wannes Rosiers <wannes.rosiers@infofarm.be>
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.
1 2 3 4 5 6 7 8 9 10 11 12 13 14 15 16 17 18 19 | ## 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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