Subclass for tuning w.r.t. fixed design points.
We simply search over a set of points fully specified by the user. The points in the design are evaluated in order as given.
This Tuner can be instantiated via the dictionary
mlr_tuners or with the associated sugar function
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In order to support general termination criteria and parallelization, we
evaluate points in a batch-fashion of size
batch_size. Larger batches mean
we can parallelize more, smaller batches imply a more fine-grained checking
of termination criteria. A batch contains of
E.g., if you set a batch size of 10 points and do a 5-fold cross validation, you can
utilize up to 50 cores.
Parallelization is supported via package future (see
section on parallelization for more details).
Maximum number of configurations to try in a batch.
Design points to try in search, one per row.
$optimize() supports progress bars via the package progressr
combined with a Terminator. Simply wrap the function in
progressr::with_progress() to enable them. We recommend to use package
progress as backend; enable with
Creates a new instance of this R6 class.
The objects of this class are cloneable with this method.
TunerDesignPoints$clone(deep = FALSE)
Whether to make a deep clone.
Package mlr3hyperband for hyperband tuning.
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library(data.table) # retrieve task task = tsk("pima") # load learner and set search space learner = lrn("classif.rpart", cp = to_tune(1e-04, 1e-1, logscale = TRUE)) # hyperparameter tuning on the pima indians diabetes data set instance = tune( method = "design_points", task = task, learner = learner, resampling = rsmp("holdout"), measure = msr("classif.ce"), design = data.table(cp = c(log(1e-1), log(1e-2))) ) # best performing hyperparameter configuration instance$result # all evaluated hyperparameter configuration as.data.table(instance$archive) # fit final model on complete data set learner$param_set$values = instance$result_learner_param_vals learner$train(task)
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