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. For the Gaussian distribution this package was found to be between 2.8 and 1679 times faster. See the vignette for more details. 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).
Any issues can be submitted to: https://github.com/mskogholt/fastNaiveBayes/issues.
Install the package with:
Or install the development version using devtools with:
rm(list = ls()) library(fastNaiveBayes) cars <- mtcars y <- as.factor(ifelse(cars$mpg > 25, "High", "Low")) x <- cars[,2:ncol(cars)] mod <- fastNaiveBayes(x, y, laplace = 1) pred <- predict(mod, newdata = x) mean(y!=pred) mod <- fnb.train(x, y, laplace = 1) pred <- predict(mod, newdata = x) mean(y!=pred) dist <- fnb.detect_distribution(x) bern <- fnb.bernoulli(x[,dist$bernoulli], y, laplace = 1) pred <- predict(bern, x[,dist$bernoulli]) mean(y!=pred) mult <- fnb.multinomial(x[,dist$multinomial], y, laplace = 1) pred <- predict(mult, x[,dist$multinomial]) mean(y!=pred) gauss <- fnb.gaussian(x[,dist$gaussian], y) pred <- predict(gauss, x[,dist$gaussian]) mean(y!=pred)
Any scripts or data that you put into this service are public.
Add the following code to your website.
For more information on customizing the embed code, read Embedding Snippets.