resample: Resample a Learner on a Task
In mllg/mlr3: Machine Learning in R - Next Generation

Description Usage Arguments Value Parallelization Logging Note Examples

Runs a resampling (possibly in parallel).

1	resample(task, learner, resampling, store_models = FALSE)

`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.

ResampleResult.

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().

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":

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

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

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

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

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

task = tsk("iris")
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))