Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.
ml_sub_models(model)ml_validation_metrics(model)
ml_cross_validator(x, estimator, estimator_param_maps, evaluator,
num_folds = 3L, collect_sub_models = FALSE, parallelism = 1L,
seed = NULL, uid = random_string("cross_validator_"), ...)
ml_train_validation_split(x, estimator, estimator_param_maps, evaluator,
train_ratio = 0.75, collect_sub_models = FALSE, parallelism = 1L,
seed = NULL, uid = random_string("train_validation_split_"), ...)
A cross validation or train-validation-split model.
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
Whether to collect a list of sub-models trained during tuning.
If set to FALSE, then only the single best sub-model will be available after fitting.
If set to true, then all sub-models will be available. Warning: For large models, collecting
all sub-models can cause OOMs on the Spark driver.
The number of threads to use when running parallel algorithms. Default is 1 for serial execution.
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.
For cross validation, ml_sub_models() returns a nested
list of models, where the first layer represents fold indices and the
second layer represents param maps. For train-validation split,
ml_sub_models() returns a list of models, corresponding to the
order of the estimator param maps.
ml_validation_metrics() returns a data frame of performance
metrics and hyperparameter combinations.
ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.