spark.naiveBayes {SparkR} | R Documentation |
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.
spark.naiveBayes(data, formula, ...) ## S4 method for signature 'SparkDataFrame,formula' spark.naiveBayes(data, formula, smoothing = 1) ## S4 method for signature 'NaiveBayesModel' summary(object) ## S4 method for signature 'NaiveBayesModel' predict(object, newData) ## S4 method for signature 'NaiveBayesModel,character' write.ml(object, path, overwrite = FALSE)
data |
a |
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 parameter. |
object |
a naive Bayes model fitted by |
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. |
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".
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
e1071: https://cran.r-project.org/package=e1071
## 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)