Spark ML – OneVsRest

R/ml_classification_one_vs_rest.R

ml_one_vs_rest

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

Arguments	Description
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.

Details

When x is a tbl_spark and formula (alternatively, response and features) is specified, the function returns a ml_model object wrapping a ml_pipeline_model which contains data pre-processing transformers, the ML predictor, and, for classification models, a post-processing transformer that converts predictions into class labels. For classification, an optional argument predicted_label_col (defaults to "predicted_label") can be used to specify the name of the predicted label column. In addition to the fitted ml_pipeline_model, ml_model objects also contain a ml_pipeline object where the ML predictor stage is an estimator ready to be fit against data. This is utilized by ml_save with type = "pipeline" to faciliate model refresh workflows.

Value

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_estimator 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.