mlr3: Machine Learning in R - next generation
"marshal"
property, which allows learners to process models so they can be serialized.
This happens automatically during resample()
and benchmark()
.default_values.Learner()
function.lgr
package.mlr_learners
respects prototype arguments
recently added in mlr3miscresample()
data.table
tests on mac.data_prototype
when resampling from learner$state
to reduce memory consumption.data.table
and BLAS to 1 when running resample()
or benchmark()
in parallel.resample()
and benchmark()
by reducing the number of hashing operations.HotstartStack
anymore when the model is missing.hotstart_threshold
are not added to the HotstartStack
anymore.learner$state$train_time
in hotstarted learners is now only the time of the last training.HotstartStack
did not work with column roles set in the task.design
of benchmark()
can now include parameter settings.packageVersion()
.col_info
to allow adding new methods for backends."mlr3.exec_chunk_bins"
option to split the resampling iterations into a number of bins.data.table()
is now re-exported."try"
, which works similar to "none"
but captures errorspaired
to benchmark_grid()
function, which can be used to create a benchmark design, where
resamplings have been instantiated on tasks.ResultData
for as_resample_result()
converter.list
for as_resample_result()
converter.print
method to make the output
more readable.distr6
.GraphLearner
.as_prediction_classif()
for data.frame()
input (#872).Learner
during train for early
stopping.mauc_aunu
, mauc_aunp
, mauc_au1u
, mauc_au1p
.classif.costs
does not require a Task
anymore.as_task_unsupervised()
mlr_reflections
.