Spark ML - OneVsRest
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. |
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()