ml-tuning {sparklyr} | R Documentation |
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_"), ...)
model |
A cross validation or train-validation-split model. |
x |
A |
estimator |
A |
estimator_param_maps |
A named list of stages and hyper-parameter sets to tune. See details. |
evaluator |
A |
num_folds |
Number of folds for cross validation. Must be >= 2. Default: 3 |
collect_sub_models |
Whether to collect a list of sub-models trained during tuning.
If set to |
parallelism |
The number of threads to use when running parallel algorithms. Default is 1 for serial execution. |
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 |
ml_cross_validator()
performs k-fold cross validation while ml_train_validation_split()
performs tuning on one pair of train and validation datasets.
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.