tune_nested {mlr3tuning} | R Documentation |
Function for Nested Resampling
Description
Function to conduct nested resampling.
Usage
tune_nested(
tuner,
task,
learner,
inner_resampling,
outer_resampling,
measure = NULL,
term_evals = NULL,
term_time = NULL,
terminator = NULL,
search_space = NULL,
store_tuning_instance = TRUE,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
allow_hotstart = FALSE,
keep_hotstart_stack = FALSE,
evaluate_default = FALSE,
callbacks = list(),
method
)
Arguments
tuner |
(Tuner)
Optimization algorithm.
|
task |
(mlr3::Task)
Task to operate on.
|
learner |
(mlr3::Learner)
Learner to tune.
|
inner_resampling |
(mlr3::Resampling)
Resampling used for the inner loop.
|
outer_resampling |
mlr3::Resampling)
Resampling used for the outer loop.
|
measure |
(mlr3::Measure)
Measure to optimize. If NULL , default measure is used.
|
term_evals |
(integer(1) )
Number of allowed evaluations.
Ignored if terminator is passed.
|
term_time |
(integer(1) )
Maximum allowed time in seconds.
Ignored if terminator is passed.
|
terminator |
(Terminator)
Stop criterion of the tuning process.
|
search_space |
(paradox::ParamSet)
Hyperparameter search space. If NULL (default), the search space is
constructed from the TuneToken of the learner's parameter set
(learner$param_set).
|
store_tuning_instance |
(logical(1) )
If TRUE (default), stores the internally created TuningInstanceSingleCrit with all intermediate results in slot $tuning_instance .
|
store_benchmark_result |
(logical(1) )
If TRUE (default), store resample result of evaluated hyperparameter
configurations in archive as mlr3::BenchmarkResult.
|
store_models |
(logical(1) )
If TRUE , fitted models are stored in the benchmark result
(archive$benchmark_result ). If store_benchmark_result = FALSE , models
are only stored temporarily and not accessible after the tuning. This
combination is needed for measures that require a model.
|
check_values |
(logical(1) )
If TRUE , hyperparameter values are checked before evaluation and
performance scores after. If FALSE (default), values are unchecked but
computational overhead is reduced.
|
allow_hotstart |
(logical(1) )
Allow to hotstart learners with previously fitted models. See also
mlr3::HotstartStack. The learner must support hotstarting. Sets
store_models = TRUE .
|
keep_hotstart_stack |
(logical(1) )
If TRUE , mlr3::HotstartStack is kept in $objective$hotstart_stack
after tuning.
|
evaluate_default |
(logical(1) )
If TRUE , learner is evaluated with hyperparameters set to their default
values at the start of the optimization.
|
callbacks |
(list of CallbackTuning)
List of callbacks.
|
method |
(character(1) )
Deprecated. Use tuner instead.
|
Value
mlr3::ResampleResult
Examples
# Nested resampling on Palmer Penguins data set
rr = tune_nested(
tuner = tnr("random_search", batch_size = 2),
task = tsk("penguins"),
learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)),
inner_resampling = rsmp ("holdout"),
outer_resampling = rsmp("cv", folds = 2),
measure = msr("classif.ce"),
term_evals = 2)
# Performance scores estimated on the outer resampling
rr$score()
# Unbiased performance of the final model trained on the full data set
rr$aggregate()
[Package
mlr3tuning version 0.20.0
Index]