Spark ML -- Naive-Bayes

Perform regression or classification using naive bayes.

ml_naive_bayes(x, response, features, lambda = 0, ml.options = ml_options(),
  ...)

Arguments

x	An object coercable to a Spark DataFrame (typically, a `tbl_spark`).
response	The name of the response vector (as a length-one character vector), or a formula, giving a symbolic description of the model to be fitted. When `response` is a formula, it is used in preference to other parameters to set the `response`, `features`, and `intercept` parameters (if available). Currently, only simple linear combinations of existing parameters is supposed; e.g. `response ~ feature1 + feature2 + ...`. The intercept term can be omitted by using `- 1` in the model fit.
features	The name of features (terms) to use for the model fit.
lambda	The (Laplace) smoothing parameter. Defaults to zero.
ml.options	Optional arguments, used to affect the model generated. See `ml_options` for more details.
...	Optional arguments. The `data` argument can be used to specify the data to be used when `x` is a formula; this allows calls of the form `ml_linear_regression(y ~ x, data = tbl)`, and is especially useful in conjunction with `do`.