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()