Package: fastNaiveBayes
Type: Package
Title: Extremely Fast Implementation of a Naive Bayes Classifier
Version: 1.1.1
Author: Martin Skogholt
Maintainer: Martin Skogholt <m.skogholt@gmail.com>
Description: This is an extremely fast implementation of a Naive Bayes classifier.
This package is currently the only package that supports a Bernoulli distribution,
a Multinomial distribution, and a Gaussian distribution, making it suitable for
both binary features, frequency counts, and numerical features. Another unique
feature is the support of a mix of different event models. Only numerical variables are allowed,
however, categorical variables can be transformed into dummies and used with
the Bernoulli distribution. This implementation offers a huge performance
gain compared to the 'e1071' implementation in R. The execution times were compared
on a data set of tweets and was found to be around 1135 times faster. Compared to other implementations
the minimum speed up was found to be 12.5 times faster for the Bernoulli distribution. See the vignette
for more details. This performance gain is only realized using a Bernoulli
event model. Furthermore, the Multinomial event model implementation is even slightly faster,
but incomparable since it was not implemented in 'e1071'. Compared to other implementations of a
Multinomial distribution, this package was found to give a speed up of 12.2 times.
The implementation is largely based on the paper
"A comparison of event models for Naive Bayes anti-spam e-mail filtering" written by
K.M. Schneider (2003) <doi:10.3115/1067807>.
Any issues can be submitted to: <https://github.com/mskogholt/fastNaiveBayes/issues>.
Depends: R (>= 3.2.0)
License: GPL-3
Encoding: UTF-8
LazyData: FALSE
Imports: Matrix, stats
Suggests: mlbench, knitr, rmarkdown, testthat
RoxygenNote: 6.1.1
VignetteBuilder: knitr
NeedsCompilation: no
Packaged: 2019-03-31 16:52:23 UTC; Martin
Repository: CRAN
Date/Publication: 2019-03-31 17:10:02 UTC

Description: This is an extremely fast implementation of a Naive Bayes classifier. This
package is currently the only package that supports a Bernoulli distribution, a Multinomial
distribution, and a Gaussian distribution, making it suitable for both binary features,
frequency counts, and numerical features. Another feature is the support of a mix of
different event models. Only numerical variables are allowed, however, categorical variables
can be transformed into dummies and used with the Bernoulli distribution. This implementation
offers a huge performance gain compared to other implementations in R. The execution times
were compared on a data set of tweets and this package was found to be around 283 to 34,841
times faster for the Bernoulli event models and 17 to 60 times faster for the Multinomial model.
See the vignette for more details. For the Gaussian distribution this package was found to be
between 2.8 and 1679 times faster. The implementation is largely based on the paper
"A comparison of event models for Naive Bayes anti-spam e-mail filtering"
written by K.M. Schneider (2003) <doi:10.3115/1067807>. Any issues can be