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chore: update to v0.0.7
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‎DESCRIPTION‎

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Package: mllrnrs
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Title: R6-Based ML Learners for 'mlexperiments'
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Version: 0.0.6.9005
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Version: 0.0.7
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Authors@R:
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person("Lorenz A.", "Kapsner", , "lorenz.kapsner@gmail.com", role = c("cre", "aut", "cph"),
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comment = c(ORCID = "0000-0003-1866-860X"))
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ranger,
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splitTools,
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testthat (>= 3.0.1),
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xgboost (>= 3.1.1.1)
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xgboost (>= 3.1.2.1)
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VignetteBuilder:
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quarto
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Config/testthat/edition: 3
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Config/testthat/parallel: false
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Date/Publication: 2025-11-16 19:33:26.761556 UTC
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Date/Publication: 2025-12-03 19:38:19.649624 UTC
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Encoding: UTF-8
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Roxygen: list(markdown = TRUE)
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SystemRequirements: Quarto command line tools

‎NEWS.md‎

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# mllrnrs NEWS
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## Unreleased (2025-11-16)
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## Unreleased (2025-12-03)
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#### Bug fixes
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#### Other changes
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- update to v0.0.7
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([70cf6db](https://github.com/kapsner/mllrnrs/tree/70cf6db4910f6ddbb2c334f4880c114f57eef87c))
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- again pinned xgboost min-version in description
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([a4f6950](https://github.com/kapsner/mllrnrs/tree/a4f6950da0eb9a828e5d7f1c14ed6cc9a8917b6b))
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([4d9b393](https://github.com/kapsner/mllrnrs/tree/4d9b3933779d6a812eecb75f08c3cd25c69167f1))
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- updated required min-version of mlexperiments
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([1114760](https://github.com/kapsner/mllrnrs/tree/1114760d15f2300f6b7edd8332412b376038b2cd))
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- updated description and news.md
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([0b24c76](https://github.com/kapsner/mllrnrs/tree/0b24c762660896447ef8ee85991c54c510601078))
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Full set of changes:
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[`v0.0.5...a4f6950`](https://github.com/kapsner/mllrnrs/compare/v0.0.5...a4f6950)
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[`v0.0.5...70cf6db`](https://github.com/kapsner/mllrnrs/compare/v0.0.5...70cf6db)
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## v0.0.5 (2025-03-05)
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‎cran-comments.md‎

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## R CMD check results
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0 errors | 0 warnings | 0 notes
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This version is compatible with the latest xgboost api changes. The reverse dependency check for 'mlsurvlrnrs' (also maintained by myself) fails currently because the pre-condition for updating 'mlsurvlrnrs' is the availability of this version of 'mllrnrs' on cran.

‎data-raw/devstuffs.R‎

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# Remove some author fields
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my_desc$del("Maintainer")
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# Set the version
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my_desc$set_version("0.0.6.9005")
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my_desc$set_version("0.0.7")
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# The title of your package
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my_desc$set(Title = "R6-Based ML Learners for 'mlexperiments'")
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# The description of your package
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usethis::use_package("lintr", type = "Suggests")
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usethis::use_package("quarto", type = "Suggests")
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usethis::use_package("glmnet", type = "Suggests")
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usethis::use_package("xgboost", type = "Suggests", min_version = "3.1.1.1")
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usethis::use_package("xgboost", type = "Suggests", min_version = "3.1.2.1")
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usethis::use_package("lightgbm", type = "Suggests", min_version = "4.0.0")
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usethis::use_package("ranger", type = "Suggests")
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usethis::use_package("splitTools", type = "Suggests")

‎vignettes/mllrnrs_xgboost_multiclass.qmd‎

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%\VignetteEncoding{UTF-8}
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%\VignetteIndexEntry{xgboost: Multiclass Classification}
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%\VignetteEngine{quarto::html}
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editor_options:
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editor_options:
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chunk_output_type: console
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execute:
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eval: false
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tuner_results_grid <- tuner$execute(k = 3)
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#>
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#> Parameter settings [============================>-------------------------------------------------------------------] 3/10 ( 30%)
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#> Parameter settings [===============================================================================================] 10/10 (100%)
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#> Parameter settings [===============================================================================================] 10/10 (100%)
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head(tuner_results_grid)
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#> setting_id metric_optim_mean nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective
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tuner_results_bayesian <- tuner$execute(k = 3)
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#>
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#> Registering parallel backend using 4 cores.
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head(tuner_results_bayesian)
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validator_results <- validator$execute()
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#>
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#> CV fold: Fold1
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#> CV fold: Fold3
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#> CV progress [========================================================================================================] 3/3 (100%)
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#>
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head(validator_results)
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#> Parameter settings [===============================================================================================] 10/10 (100%)
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#> CV fold: Fold2
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#> CV progress [====================================================================>-----------------------------------] 2/3 ( 67%)
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#> Parameter settings [===============================================================================================] 10/10 (100%)
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## Appendix I: Grid-Search with Target Weigths
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Here, `xgboost`'s [`weight`-argument](https://rdrr.io/cran/xgboost/man/setinfo.html) is used to rescale the case-weights during the training.
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Here, `xgboost`'s [`weight`-argument](https://rdrr.io/cran/xgboost/man/xgb.DMatrix.html) is used to rescale the case-weights during the training.
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```{r}
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#> fold performance nrounds subsample colsample_bytree min_child_weight learning_rate max_depth objective eval_metric num_class

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