naivebayes: naivebayes

In naivebayes: High Performance Implementation of the Naive Bayes Algorithm

Description

The naivebayes package provides an efficient implementation of the popular Naive Bayes classifier in R. It was developed and is now maintained based on three principles: it should be efficient, user friendly and written in Base R. The last implies no dependencies, however, it neither denies nor interferes with being efficient as many functions from the Base R distribution use highly efficient routines programmed in lower level languages, such as C or FORTRAN. In fact, the naivebayes package utilizes only such functions for resource-intensive calculations.

Details

The general function naive_bayes() detects the class of each feature in the dataset and, depending on the user choices, assumes possibly different distribution for each feature. It currently supports following class conditional distributions:

• categorical distribution for discrete features

• Poisson distribution for non-negative integers

• Gaussian distribution for continuous features

• non-parametrically estimated densities via Kernel Density Estimation for continuous features

In addition to that specialized functions are available which implement:

• Bernoulli Naive Bayes via bernoulli_naive_bayes()

• Multinomial Naive Bayes via multinomial_naive_bayes()

• Poisson Naive Bayes via poisson_naive_bayes()

• Gaussian Naive Bayes via gaussian_naive_bayes()

• Non-Parametric Naive Bayes via nonparametric_naive_bayes()

Extended documentation can be found on the website:

• https://majkamichal.github.io/naivebayes/

Bug reports:

• https://github.com/majkamichal/naivebayes/issues

Suggestions:

• [e-mail]

naivebayes documentation built on March 13, 2020, 1:31 a.m.