Spark ML -- OneVsRest

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.

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



A spark_connection, ml_pipeline, or a tbl_spark.


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.


Object of class ml_estimator. Base binary classifier that we reduce multiclass classification into.


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 column name. The column should be a numeric column. Usually this column is output by ft_r_formula.


Prediction column name.


A character string used to uniquely identify the ML estimator.


Optional arguments; currently unused.


The object returned depends on the class of x.

  • spark_connection: When x is a spark_connection, the function returns an instance of a ml_predictor object. The object contains a pointer to a Spark Predictor object and can be used to compose Pipeline objects.

  • ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with the predictor appended to the pipeline.

  • tbl_spark: When x is a tbl_spark, a predictor is constructed then immediately fit with the input tbl_spark, returning a prediction model.

  • tbl_spark, with formula: specified When formula is specified, the input tbl_spark is first transformed using a RFormula transformer before being fit by the predictor. The object returned in this case is a ml_model which is a wrapper of a ml_pipeline_model.

See also

See for more information on the set of supervised learning algorithms.

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