Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.
ml_cross_validator(x, estimator, estimator_param_maps, evaluator,
num_folds = 3L, seed = NULL, uid = random_string("cross_validator_"),
...)ml_train_validation_split(x, estimator, estimator_param_maps, evaluator,
train_ratio = 0.75, seed = NULL,
uid = random_string("train_validation_split_"), ...)
A spark_connection, ml_pipeline, or a tbl_spark.
A ml_estimator object.
A named list of stages and hyper-parameter sets to tune. See details.
A ml_evaluator object, see ml_evaluator.
Number of folds for cross validation. Must be >= 2. Default: 3
A random seed. Set this value if you need your results to be reproducible across repeated calls.
A character string used to uniquely identify the ML estimator.
Optional arguments; currently unused.
Ratio between train and validation data. Must be between 0 and 1. Default: 0.75
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_cross_validator or ml_traing_validation_split object.
ml_pipeline: When x is a ml_pipeline, the function returns a ml_pipeline with
the tuning estimator appended to the pipeline.
tbl_spark: When x is a tbl_spark, a tuning estimator is constructed then
immediately fit with the input tbl_spark, returning a ml_cross_validation_model or a
ml_train_validation_split_model object.
ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.