ml_one_vs_rest: Spark ML - OneVsRest

In rstudio/sparklyr: R Interface to Apache Spark

Spark ML – OneVsRest

Description

Reduction of Multiclass Classification to Binary Classification. Performs reduction using one against all strategy. For a multiclass classification with k classes, train k models (one per class). Each example is scored against all k models and the model with highest score is picked to label the example.

Usage

ml_one_vs_rest(
  x,
  formula = NULL,
  classifier = NULL,
  features_col = "features",
  label_col = "label",
  prediction_col = "prediction",
  uid = random_string("one_vs_rest_"),
  ...
)

Arguments

x	A spark_connection, ml_pipeline, or a tbl_spark.
formula	Used when x is a tbl_spark. R formula as a character string or a formula. This is used to transform the input dataframe before fitting, see ft_r_formula for details.
classifier	Object of class ml_estimator. Base binary classifier that we reduce multiclass classification into.
features_col	Features column name, as a length-one character vector. The column should be single vector column of numeric values. Usually this column is output by ft_r_formula.
label_col	Label column name. The column should be a numeric column. Usually this column is output by ft_r_formula.
prediction_col	Prediction column name.
uid	A character string used to uniquely identify the ML estimator.
...	Optional arguments; see Details.

Value

The object returned depends on the class of x. If it is a spark_connection, the function returns a ml_estimator object. If it is a ml_pipeline, it will return a pipeline with the predictor appended to it. If a tbl_spark, it will return a tbl_spark with the predictions added to it.

See Also

Other ml algorithms: ml_aft_survival_regression(), ml_decision_tree_classifier(), ml_gbt_classifier(), ml_generalized_linear_regression(), ml_isotonic_regression(), ml_linear_regression(), ml_linear_svc(), ml_logistic_regression(), ml_multilayer_perceptron_classifier(), ml_naive_bayes(), ml_random_forest_classifier()

rstudio/sparklyr documentation built on March 29, 2024, 3:30 p.m.