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.

See also

Other Spark ML routines: ml_als_factorization, ml_decision_tree, ml_generalized_linear_regression, ml_gradient_boosted_trees, ml_kmeans, ml_lda, ml_linear_regression, ml_logistic_regression, ml_multilayer_perceptron, ml_one_vs_rest, ml_pca, ml_random_forest, ml_survival_regression