ml_naive_bayes
Spark ML -- Naive-Bayes
Perform regression or classification using naive bayes.
Usage
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 theresponse
,features
, andintercept
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 whenx
is a formula; this allows calls of the formml_linear_regression(y ~ x, data = tbl)
, and is especially useful in conjunction withdo
.
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