mlr_tuners_internal | R Documentation |
Subclass to conduct only internal hyperparameter tuning for a mlr3::Learner.
This Tuner can be instantiated with the associated sugar function tnr()
:
tnr("internal")
$optimize()
supports progress bars via the package progressr
combined with a bbotk::Terminator. Simply wrap the function in
progressr::with_progress()
to enable them. We recommend to use package
progress as backend; enable with progressr::handlers("progress")
.
All Tuners use a logger (as implemented in lgr) from package
bbotk.
Use lgr::get_logger("bbotk")
to access and control the logger.
There are several sections about hyperparameter optimization in the mlr3book.
Getting started with hyperparameter optimization.
An overview of all tuners can be found on our website.
Tune a support vector machine on the Sonar data set.
Learn about tuning spaces.
Estimate the model performance with nested resampling.
Learn about multi-objective optimization.
Simultaneously optimize hyperparameters and use early stopping with XGBoost.
Automate the tuning.
The gallery features a collection of case studies and demos about optimization.
Learn more advanced methods with the Practical Tuning Series.
Learn about hotstarting models.
Run the default hyperparameter configuration of learners as a baseline.
Use the Hyperband optimizer with different budget parameters.
The cheatsheet summarizes the most important functions of mlr3tuning.
mlr3tuning::Tuner
-> mlr3tuning::TunerBatch
-> TunerBatchInternal
new()
Creates a new instance of this R6 class.
TunerBatchInternal$new()
clone()
The objects of this class are cloneable with this method.
TunerBatchInternal$clone(deep = FALSE)
deep
Whether to make a deep clone.
The selected mlr3::Measure does not influence the tuning result. To change the loss-function for the internal tuning, consult the hyperparameter documentation of the tuned mlr3::Learner.
Other Tuner:
Tuner
,
mlr_tuners
,
mlr_tuners_cmaes
,
mlr_tuners_design_points
,
mlr_tuners_gensa
,
mlr_tuners_grid_search
,
mlr_tuners_irace
,
mlr_tuners_nloptr
,
mlr_tuners_random_search
library(mlr3learners)
# Retrieve task
task = tsk("pima")
# Load learner and set search space
learner = lrn("classif.xgboost",
nrounds = to_tune(upper = 1000, internal = TRUE),
early_stopping_rounds = 10,
validate = "test",
eval_metric = "merror"
)
# Internal hyperparameter tuning on the pima indians diabetes data set
instance = tune(
tnr("internal"),
tsk("iris"),
learner,
rsmp("cv", folds = 3),
msr("internal_valid_score", minimize = TRUE, select = "merror")
)
# best performing hyperparameter configuration
instance$result_learner_param_vals
instance$result_learner_param_vals$internal_tuned_values
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