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 posts gives an overview by listing the recent release notes of mlr3 packages from the last quarter. Note that only CRAN packages are listed here and the sort order is alphabetically.
Interval: 2021-10-01 - 2021-12-31
Description: Machine Learning in R - Next Generation
(classif|regr|surv).xgboost with hyperparameter nrounds updated) can now optionally store a stack of trained learners to be used to hotstart their training. Note that this feature is still somewhat experimental. See HotstartStack and #719.sim.jaccard (Jaccard Index) and sim.phi (Phi coefficient) (#690).predict_newdata() now also supports DataBackend as input.install_pkgs() to install required packages. This generic works for all objects with a packages field as well as ResampleResult and BenchmarkResult (#728).regr.debug for debugging.Task method $set_levels() to control how data with factor columns is returned, independent of the used DataBackend.NA if prerequisite are not met (#699). This allows to conveniently score your experiments with multiple measures having different requirements.%.Description: Analysis and Visualisation of Benchmark Experiments
Description: Data Base Backend for ‘mlr3’
Description: Recommended learners for mlr3
eval_metric() is now explicitly set for xgboost learners to silence a deprecation warning.mtry.ratio is converted to mtry to simplify tuning.Description: Preprocessing Operators and Pipelines for ‘mlr3’
%>>!% that modifies Graphs in-place.chain_graphs(), concat_graphs(), Graph$chain() as alternatives for %>>% and %>>!%.pos() and ppls() which create lists of PipeOps/Graphs and can be seen as “plural” forms of po() and ppl().po() S3-method for PipeOp class that clones a PipeOp object and optionally modifies its attributes.Graph$add_pipeop() now clones the PipeOp being added.GraphLearner class, which gets the trained graph.as_learner() S3-method for PipeOp class that turns wraps a PipeOp in a Graph and turns that into a Learner.PipeOpHistBin: renamed ‘bins’ Param to ‘breaks’PipeOpImputeHist: fix handling of integer features spanning the entire represented integer rangePipeOpImputeOOR: fix handling of integer features spanning the entire represented integer rangePipeOpProxy: Avoid unnecessary clonePipeOpScale: Performance improvementDescription: Probabilistic Supervised Learning for ‘mlr3’
Description: Support for Spatial Objects Within the ‘mlr3’ Ecosystem
Description: Search Spaces for Hyperparameter Tuning
Description: Visualizations for ‘mlr3’
For attribution, please cite this work as
Schratz (2022, Jan. 3). mlr-org: mlr3 Package Updates - Q4/2021. Retrieved from https://mlr-org.github.io/mlr-org-website/posts/2022-01-03-mlr3-package-updates-q42021/
BibTeX citation
@misc{schratz2022mlr3,
author = {Schratz, Patrick},
title = {mlr-org: mlr3 Package Updates - Q4/2021},
url = {https://mlr-org.github.io/mlr-org-website/posts/2022-01-03-mlr3-package-updates-q42021/},
year = {2022}
}