This posts gives an overview by listing the recent release notes of mlr3 packages from the last quarter.
Due to the high amount of packages in the mlr3 ecosystem, it is hard to keep up with the latest changes across all packages. This post tries to tackle this issue by listing all release notes of the packages most recent releases in the last quarter. Note that only CRAN packages are listed here and the sort order is alphabetically.
Interval: 2021-07-01 - 2021-10-01
Description: Black-Box Optimization Toolkit
Archive.TerminatorEvals with an additional hyperparameter k to define the budget depending on the dimension of the search space.bb_optimize() function for quick optimization.OptimizerIrace from irace package.Description: Machine Learning in R - Next Generation
Task$label(). These will be used in visualizations in the future.Task$add_strata().partition() to split a task into a training and test set.loglik() for class Learner."aic" and "bic" to compute the Akaike Information Criterion or the Bayesian Information Criterion, respectively.ResamplingCustomCV. Creates a custom resampling split based on the levels of a user-provided factor variable.encapsulate for resample() and benchmark() to conveniently enable encapsulation and also set the fallback learner to the featureless learner. This is simply for convenience, configuring each learner individually is still possible and allows a more fine-grained control (#634, #642).parallel_predict for Learner to enable parallel predictions via the future backend. This currently is only enabled while calling the $predict() or $predict_newdata methods and is disabled during resample() and benchmark() where you have other means to parallelize.$data in ResampleResult and BenchmarkResult to simplify the API and avoid confusion. The converter as.data.table() can be used instead to access the internal data.beta.ordered in Task$data() from TRUE to FALSE.Description: Cluster Extension for ‘mlr3’
Description: Filter Based Feature Selection for ‘mlr3’
nfeat was not passed down to {praznik} filters (#97)Description: Recommended Learners for ‘mlr3’
mtry.ratio is converted to mtry to simplify tuning.Description: Preprocessing Operators and Pipelines for ‘mlr3’
Description: Spatiotemporal Resampling Methods for ‘mlr3’
autoplot(): removed argument crs. The CRS is now inferred from the supplied Task. Setting a different CRS than the task might lead to spurious issues and the initial idea of changing the CRS for plotting to have proper axes labeling does not apply (anymore) (#144)autoplot() support for ResamplingCustomCV (#140)"spcv_block": Assert error if folds > 2 when selection = "checkerboard" (#150)TaskRegrST tasks from sf objects (#152)"spcv_block" since it is required in {blockCV} >= 2.1.4 and {sf} >= 1.0Description: Tuning for ‘mlr3’
AutoTuner$base_learner() method to extract the base learner from nested learner objects.tune() supports multi-criteria tuning.TunerIrace from irace package.extract_inner_tuning_archives() helper function to extract inner tuning archives.ArchiveTuning$extended_archive() method. The mlr3::ResampleResults are joined automatically by as.data.table.TuningArchive() and extract_inner_tuning_archives().Description: Easily Install and Load the ‘mlr3’ Package Family
Description: Visualizations for ‘mlr3’
PredictionClassif.For attribution, please cite this work as
Schratz (2021, Sept. 29). mlr-org: mlr3 Package Updates - Q3/2021. Retrieved from https://mlr-org.github.io/mlr-org-website/posts/2021-09-29-mlr3-package-updates-q32021/
BibTeX citation
@misc{schratz2021mlr3,
author = {Schratz, Patrick},
title = {mlr-org: mlr3 Package Updates - Q3/2021},
url = {https://mlr-org.github.io/mlr-org-website/posts/2021-09-29-mlr3-package-updates-q32021/},
year = {2021}
}