ti | R Documentation |
Function to construct a TuningInstanceBatchSingleCrit or TuningInstanceBatchMultiCrit.
ti(
task,
learner,
resampling,
measures = NULL,
terminator,
search_space = NULL,
internal_search_space = NULL,
store_benchmark_result = TRUE,
store_models = FALSE,
check_values = FALSE,
callbacks = NULL
)
task |
(mlr3::Task) |
learner |
(mlr3::Learner) |
resampling |
(mlr3::Resampling) |
measures |
(mlr3::Measure or list of mlr3::Measure) |
terminator |
(bbotk::Terminator) |
search_space |
(paradox::ParamSet) |
internal_search_space |
(paradox::ParamSet or |
store_benchmark_result |
( |
store_models |
( |
check_values |
( |
callbacks |
(list of mlr3misc::Callback) |
There are several sections about hyperparameter optimization in the mlr3book.
Getting started with hyperparameter optimization.
Tune a simple classification tree on the Sonar data set.
Learn about tuning spaces.
The gallery features a collection of case studies and demos about optimization.
Learn more advanced methods with the practical tuning series.
Simultaneously optimize hyperparameters and use early stopping with XGBoost.
Make us of proven search space.
Learn about hotstarting models.
Run the default hyperparameter configuration of learners as a baseline.
If no measure is passed, the default measure is used. The default measure depends on the task type.
Task | Default Measure | Package |
"classif" | "classif.ce" | mlr3 |
"regr" | "regr.mse" | mlr3 |
"surv" | "surv.cindex" | mlr3proba |
"dens" | "dens.logloss" | mlr3proba |
"classif_st" | "classif.ce" | mlr3spatial |
"regr_st" | "regr.mse" | mlr3spatial |
"clust" | "clust.dunn" | mlr3cluster |
# Hyperparameter optimization on the Palmer Penguins data set
task = tsk("penguins")
# Load learner and set search space
learner = lrn("classif.rpart",
cp = to_tune(1e-04, 1e-1, logscale = TRUE)
)
# Construct tuning instance
instance = ti(
task = task,
learner = learner,
resampling = rsmp("cv", folds = 3),
measures = msr("classif.ce"),
terminator = trm("evals", n_evals = 4)
)
# Choose optimization algorithm
tuner = tnr("random_search", batch_size = 2)
# Run tuning
tuner$optimize(instance)
# Set optimal hyperparameter configuration to learner
learner$param_set$values = instance$result_learner_param_vals
# Train the learner on the full data set
learner$train(task)
# Inspect all evaluated configurations
as.data.table(instance$archive)
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