In majkamichal/naivebayes: High Performance Implementation of the Naive Bayes Algorithm

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Naïve Bayes


Overview

The naivebayes package presents an efficient implementation of the widely-used Naïve Bayes classifier. It upholds three core principles: efficiency, user-friendliness, and reliance solely on Base R. By adhering to the latter principle, the package ensures stability and reliability without introducing external dependencies^[Specialized Naïve Bayes functions within the package may optionally utilize sparse matrices if the Matrix package is installed. However, the Matrix package is not a dependency, and users are not required to install or use it.]. This design choice maintains efficiency by leveraging the optimized routines inherent in Base R, many of which are programmed in high-performance languages like C/C++ or FORTRAN. By following these principles, the naivebayes package provides a reliable and efficient tool for Naïve Bayes classification tasks, ensuring that users can perform their analyses effectively and with ease.

The naive_bayes() function is designed to determine the class of each feature in a dataset, and depending on user specifications, it can assume various distributions for each feature. It currently supports the following class conditional distributions:

• categorical distribution for discrete features (with Bernoulli distribution as a special case for binary outcomes)
• Poisson distribution for non-negative integer features
• 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()

These specialized functions are carefully optimized for efficiency, utilizing linear algebra operations to excel when handling dense matrices. Additionally, they can also exploit sparsity of matrices for enhanced performance and work in presence of missing data. The package also includes various helper functions to improve user experience. Moreover, users can access the general naive_bayes() function through the excellent Caret package, providing additional versatility.

Installation

The naivebayes package can be installed from the CRAN repository by simply executing in the console the following line:

install.packages("naivebayes")

# Or the the development version from GitHub:
devtools::install_github("majkamichal/naivebayes")

majkamichal/naivebayes documentation built on March 26, 2024, 8:44 p.m.