mlr3: resample – R documentation

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resample

Resample a Learner on a Task

Description

Runs a resampling (possibly in parallel): Repeatedly apply Learner learner on a training set of Task task to train a model, then use the trained model to predict observations of a test set. Training and test sets are defined by the Resampling resampling.

Usage

resample(
  task,
  learner,
  resampling,
  store_models = FALSE,
  store_backends = TRUE
)

Arguments

`task`	(Task).
`learner`	(Learner).
`resampling`	(Resampling).
`store_models`	(`logical(1)`) Keep the fitted model after the test set has been predicted? Set to `TRUE` if you want to further analyse the models or want to extract information like variable importance.
`store_backends`	(`logical(1)`) Keep the DataBackend of the Task in the ResampleResult? Set to `TRUE` if your performance measures require a Task, or to analyse results more conveniently. Set to `FALSE` to reduce the file size and memory footprint after serialization. The current default is `TRUE`, but this eventually will be changed in a future release.

Value

ResampleResult.

Parallelization

This function can be parallelized with the future package. One job is one resampling iteration, and all jobs are send to an apply function from future.apply in a single batch. To select a parallel backend, use future::plan().

Progress Bars

This function supports progress bars via the package progressr. Simply wrap the function in progressr::with_progress() to enable them. We recommend to use package progress as backend; enable with progressr::handlers("progress").

Logging

The mlr3 uses the lgr package for logging. lgr supports multiple log levels which can be queried with getOption("lgr.log_levels").

To suppress output and reduce verbosity, you can lower the log from the default level "info" to "warn":

lgr::get_logger("mlr3")$set_threshold("warn")

To get additional log output for debugging, increase the log level to "debug" or "trace":

lgr::get_logger("mlr3")$set_threshold("debug")

To log to a file or a data base, see the documentation of lgr::lgr-package.

Note

The fitted models are discarded after the predictions have been computed in order to reduce memory consumption. If you need access to the models for later analysis, set store_models to TRUE.

Examples

task = tsk("penguins")
learner = lrn("classif.rpart")
resampling = rsmp("cv")

# Explicitly instantiate the resampling for this task for reproduciblity
set.seed(123)
resampling$instantiate(task)

rr = resample(task, learner, resampling)
print(rr)

# Retrieve performance
rr$score(msr("classif.ce"))
rr$aggregate(msr("classif.ce"))

# merged prediction objects of all resampling iterations
pred = rr$prediction()
pred$confusion

# Repeat resampling with featureless learner
rr_featureless = resample(task, lrn("classif.featureless"), resampling)

# Convert results to BenchmarkResult, then combine them
bmr1 = as_benchmark_result(rr)
bmr2 = as_benchmark_result(rr_featureless)
print(bmr1$combine(bmr2))

mlr3

Machine Learning in R - Next Generation

v0.11.0

LGPL-3

Authors

Michel Lang [cre, aut] (<https://orcid.org/0000-0001-9754-0393>), Bernd Bischl [aut] (<https://orcid.org/0000-0001-6002-6980>), Jakob Richter [aut] (<https://orcid.org/0000-0003-4481-5554>), Patrick Schratz [aut] (<https://orcid.org/0000-0003-0748-6624>), Giuseppe Casalicchio [ctb] (<https://orcid.org/0000-0001-5324-5966>), Stefan Coors [ctb] (<https://orcid.org/0000-0002-7465-2146>), Quay Au [ctb] (<https://orcid.org/0000-0002-5252-8902>), Martin Binder [aut], Marc Becker [ctb] (<https://orcid.org/0000-0002-8115-0400>)

Initial release