ml_naive_bayes

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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 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; 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

Aliases
  • ml_naive_bayes
Documentation reproduced from package sparklyr, version 0.4, License: Apache License 2.0 | file LICENSE

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