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 = NULL,
features_col = "features",
label_col = "label",
prediction_col = "prediction",
uid = random_string("one_vs_rest_"),
...
)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.
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; see Details.
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()