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.
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.
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.
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.
See https://spark.apache.org/docs/latest/ml-classification-regression.html 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()