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, ...)
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.
...
Optional arguments; currently unused.
See Also

Other Spark ML routines: 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 0.2.30, License: file LICENSE

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