R: Naive Bayes Models

spark.naiveBayes {SparkR}

R Documentation

Naive Bayes Models

Description

spark.naiveBayes fits a Bernoulli naive Bayes model against a SparkDataFrame. Users can call summary to print a summary of the fitted model, predict to make predictions on new data, and write.ml/read.ml to save/load fitted models. Only categorical data is supported.

Usage

spark.naiveBayes(data, formula, ...)

## S4 method for signature 'NaiveBayesModel'
predict(object, newData)

## S4 method for signature 'NaiveBayesModel'
summary(object)

## S4 method for signature 'SparkDataFrame,formula'
spark.naiveBayes(data, formula,
  smoothing = 1)

## S4 method for signature 'NaiveBayesModel,character'
write.ml(object, path,
  overwrite = FALSE)

Arguments

`data`	a `SparkDataFrame` of observations and labels for model fitting.
`formula`	a symbolic description of the model to be fitted. Currently only a few formula operators are supported, including '~', '.', ':', '+', and '-'.
`...`	additional argument(s) passed to the method. Currently only `smoothing`.
`object`	a naive Bayes model fitted by `spark.naiveBayes`.
`newData`	a SparkDataFrame for testing.
`smoothing`	smoothing parameter.
`path`	the directory where the model is saved.
`overwrite`	overwrites or not if the output path already exists. Default is FALSE which means throw exception if the output path exists.

Value

predict returns a SparkDataFrame containing predicted labeled in a column named "prediction".

summary returns summary information of the fitted model, which is a list. The list includes apriori (the label distribution) and tables (conditional probabilities given the target label).

spark.naiveBayes returns a fitted naive Bayes model.

Note

predict(NaiveBayesModel) since 2.0.0

summary(NaiveBayesModel) since 2.0.0

spark.naiveBayes since 2.0.0

write.ml(NaiveBayesModel, character) since 2.0.0

Examples

## Not run: 
##D data <- as.data.frame(UCBAdmissions)
##D df <- createDataFrame(data)
##D 
##D # fit a Bernoulli naive Bayes model
##D model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0)
##D 
##D # get the summary of the model
##D summary(model)
##D 
##D # make predictions
##D predictions <- predict(model, df)
##D 
##D # save and load the model
##D path <- "path/to/model"
##D write.ml(model, path)
##D savedModel <- read.ml(path)
##D summary(savedModel)
## End(Not run)

[Package SparkR version 2.1.1 Index]