SparkR (version 2.3.0)

spark.naiveBayes: 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 SparkDataFrame,formula
spark.naiveBayes(data, formula,
  smoothing = 1, handleInvalid = c("error", "keep", "skip"))
# S4 method for NaiveBayesModel
summary(object)
# S4 method for NaiveBayesModel
predict(object, newData)
# S4 method for 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.

smoothing

smoothing parameter.

handleInvalid

How to handle invalid data (unseen labels or NULL values) in features and label column of string type. Supported options: "skip" (filter out rows with invalid data), "error" (throw an error), "keep" (put invalid data in a special additional bucket, at index numLabels). Default is "error".

object

a naive Bayes model fitted by spark.naiveBayes.

newData

a SparkDataFrame for testing.

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

spark.naiveBayes returns a fitted naive Bayes model.

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).

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

See Also

e1071: https://cran.r-project.org/package=e1071

write.ml

Examples

# NOT RUN {
data <- as.data.frame(UCBAdmissions)
df <- createDataFrame(data)

# fit a Bernoulli naive Bayes model
model <- spark.naiveBayes(df, Admit ~ Gender + Dept, smoothing = 0)

# get the summary of the model
summary(model)

# make predictions
predictions <- predict(model, df)

# save and load the model
path <- "path/to/model"
write.ml(model, path)
savedModel <- read.ml(path)
summary(savedModel)
# }