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)
} # }