ml_naive_bayes
From sparklyr v0.3.2
by Javier Luraschi
Spark ML -- Naive-Bayes
Perform regression or classification using naive bayes.
Usage
ml_naive_bayes(x, response, features, lambda = 0, ...)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
responseis a formula, it is used in preference to other parameters to set theresponse,features, andinterceptparameters (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- 1in the model fit. - features
- The name of features (terms) to use for the model fit.
- lambda
- The (Laplace) smoothing parameter. Defaults to zero.
- ...
- Optional arguments; currently unused.
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
Community examples
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