# Naive Bayes Models

`spark.naiveBayes.Rd`

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

## Note

spark.naiveBayes since 2.0.0

summary(NaiveBayesModel) since 2.0.0

predict(NaiveBayesModel) since 2.0.0

write.ml(NaiveBayesModel, character) since 2.0.0

## Examples

```
if (FALSE) { # \dontrun{
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)
} # }
```