Spark ML -- One vs Rest

Perform regression or classification using one vs rest.

ml_one_vs_rest(x, classifier, response, features, ml.options = ml_options(),
  ...)

Arguments

x

An object coercable to a Spark DataFrame (typically, a tbl_spark).

classifier

The classifier model. These model objects can be obtained through the use of the only.model parameter supplied with ml_options.

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.

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_naive_bayes, ml_pca, ml_random_forest, ml_survival_regression