ml-tuning

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Spark ML -- Tuning

Perform hyper-parameter tuning using either K-fold cross validation or train-validation split.

Usage
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_"), ...)

Arguments
x

A spark_connection, ml_pipeline, or a tbl_spark.

estimator

A ml_estimator object.

estimator_param_maps

A named list of stages and hyper-parameter sets to tune. See details.

evaluator

A ml_evaluator object, see ml_evaluator.

num_folds

Number of folds for cross validation. Must be >= 2. Default: 3

seed

A random seed. Set this value if you need your results to be reproducible across repeated calls.

uid

A character string used to uniquely identify the ML estimator.

...

Optional arguments; currently unused.

train_ratio

Ratio between train and validation data. Must be between 0 and 1. Default: 0.75

Details

ml_cross_validator() performs k-fold cross validation while ml_train_validation_split() performs tuning on one pair of train and validation datasets.

Value

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.

Aliases
  • ml-tuning
  • ml_cross_validator
  • ml_train_validation_split
Documentation reproduced from package sparklyr, version 0.7.0, License: Apache License 2.0 | file LICENSE

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