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mlr3tuning vs mlr3viz

A side-by-side editorial comparison of mlr3tuning and mlr3viz — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:mlr3r-package

mlr3tuning vs mlr3viz: at a glance

Featuremlr3tuningmlr3viz
SectorAnalyticsAnalytics
Velocity score2.52.5
Sparks · 30d00
Top themesmlr3, hyperparameter-tuning, async-optimization, callbacksmlr3, visualization, ggplot2, roc-curves
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is mlr3tuning?

mlr3tuning is rebuilding its async machinery under a stable public surface

mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.

Read the full mlr3tuning trajectory →

What is mlr3viz?

mlr3viz keeps the ecosystem's plots working while the plots themselves move out

mlr3viz supplies autoplot methods across mlr3 objects — learners, resample and benchmark results, tuning instances, ensemble feature-selection results. Recent releases are mostly defensive: suppressing ggplot2::fortify() warnings on ROC and PRC curves, pinning legend order so plots are deterministic across ggplot2 environments, and tracking mlr3 1.7.2. A visible piece of scope also left, with the LearnerSurvCoxPH plot moving to mlr3proba.

Read the full mlr3viz trajectory →

mlr3tuning vs mlr3viz: editorial side-by-side

M
mlr3tuning
ANALYTICS
2.5

mlr3tuning is rebuilding its async machinery under a stable public surface

◆ Current state

mlr3tuning provides hyperparameter optimization for the mlr3 ecosystem, and its recent history is dominated by the asynchronous tuning path: archive freezing, callback stages around queue evaluation, and version-locked compatibility with the rush backend. Releases pair a small feature with several fixes and an explicit compatibility line naming the mlr3 or rush version they track. The most recent release drops all workarounds for older rush versions, which suggests that dependency has stabilized enough to require rather than accommodate.

◆ Where it's heading

Two things are being tidied at once. The async archive is converging on a consistent data.table representation across batch and async variants, so results are shaped the same regardless of how tuning ran. Separately, the package is becoming a better ecosystem citizen — unioning tuner properties on load instead of overwriting them, removing its callbacks on unload, and raising informative errors from AutoTuner accessors on an untrained model. Both are the marks of a package used as a dependency more than as a destination.

◆ Prediction

With rush pinned to 1.2.0 and the compatibility shims gone, the next release is likely to expose more of the async path through callbacks rather than change the tuning interface.

M
mlr3viz
ANALYTICS
2.5

mlr3viz keeps the ecosystem's plots working while the plots themselves move out

◆ Current state

mlr3viz supplies autoplot methods across mlr3 objects — learners, resample and benchmark results, tuning instances, ensemble feature-selection results. Recent releases are mostly defensive: suppressing ggplot2::fortify() warnings on ROC and PRC curves, pinning legend order so plots are deterministic across ggplot2 environments, and tracking mlr3 1.7.2. A visible piece of scope also left, with the LearnerSurvCoxPH plot moving to mlr3proba.

◆ Where it's heading

The package is being narrowed toward generic plotting infrastructure while learner-specific plots migrate to the packages that own those learners. What it does add is access rather than new charts — passing parameters through to precrec::autoplot(), better hints when the wrong autoplot type is requested, and a confidence-interval plot for mlr3inferr. Determinism across ggplot2 versions has become a recurring concern, which is what happens when a visualization package is depended on by documentation and tests.

◆ Prediction

Following the Cox proportional-hazards precedent, further learner-specific plots are likely to move to their owning packages, leaving mlr3viz with the cross-cutting result objects.

Alternatives to mlr3tuning and mlr3viz

Other Analytics products tracked by Sparkpulse, ranked by recent ship velocity. Each card links to a full editorial trajectory and lets you pivot into a head-to-head comparison with either mlr3tuning or mlr3viz.

See all mlr3tuning alternatives → · See all mlr3viz alternatives →

Recent activity from mlr3tuning and mlr3viz

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 18d agomlr3tuningmlr3tuning 1.6.1 stops clobbering other packages' tuner properties
  2. 18d agomlr3vizmlr3viz 0.11.1 pins legend order for deterministic plots
  3. 4mo agomlr3tuningmlr3tuning 1.6.0 aligns archive column order across tuning classes
  4. 5mo agomlr3vizmlr3viz 0.11.0 quiets ggplot2 fortify warnings on ROC curves
  5. 8mo agomlr3tuningmlr3tuning 1.5.1 tracks xgboost 3.1.2.1
  6. 8mo agomlr3tuningmlr3tuning 1.5.0 adds queue evaluation stages to async callbacks
  7. 1y agomlr3vizmlr3viz 0.10.1 passes plotting parameters through to precrec
  8. 1y agomlr3tuningmlr3tuning 1.4.0 unifies logging under a base mlr3 logger
  9. 1y agomlr3tuningmlr3tuning 1.3.0 adds a frozen async archive and leaner worker storage
  10. 1y agomlr3vizmlr3viz 0.10.0 adds a LearnerSurvCoxPH plot
  11. 2y agomlr3vizmlr3viz 0.9.0 adds EnsembleFSResult plots
  12. 2y agomlr3vizmlr3viz 0.8.0 tracks paradox 1.0.0

Frequently asked questions

What is the difference between mlr3tuning and mlr3viz?

Both compete on the same themes — mlr3, r-package — within Analytics. mlr3tuning and mlr3viz are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.

Is mlr3tuning better than mlr3viz?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3tuning and mlr3viz are shipping at a similar cadence (velocity 2.5 vs 2.5, both within Sparkpulse's "active" band). For your specific use case, the alternatives sections above list other Analytics products to evaluate alongside.

What are the best alternatives to mlr3tuning?

Top mlr3tuning alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3tuning alternatives" section above for the current picks, or visit /alternatives/mlr3tuning for the full list with editorial commentary on each.

What are the best alternatives to mlr3viz?

Top mlr3viz alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3viz alternatives" section above for the current picks, or visit /alternatives/mlr3viz for the full list with editorial commentary on each.