← Back to home
Comparison · ai-assistants

mlr3hyperband vs mlr3tuningspaces

A side-by-side editorial comparison of mlr3hyperband and mlr3tuningspaces — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:hyperparameter-tuningmlr3r-packagemachine-learning

mlr3hyperband vs mlr3tuningspaces: at a glance

Featuremlr3hyperbandmlr3tuningspaces
Sectorai-assistantsai-assistants
Velocity score2.52.5
Sparks · 30d00
Top themeshyperparameter-tuning, mlr3, asynchronous-optimization, r-packagehyperparameter-tuning, mlr3, benchmark-studies, r-package
Last editorial update2h ago2h ago
WebsiteVisit →Visit →

What is mlr3hyperband?

Hyperband tuning for mlr3, now built on an asynchronous backend it treats as mandatory

mlr3hyperband supplies successive-halving and Hyperband optimizers to the mlr3 tuning stack. Its recent releases are dominated by ecosystem plumbing rather than new search algorithms: a hard floor of `rush` 1.0.0, alignment with mlr3 1.7.2, and a move onto the ecosystem's new base logger. The last genuinely new optimizer was `OptimizerAsyncSuccessiveHalving` in 1.0.0.

Read the full mlr3hyperband trajectory →

What is mlr3tuningspaces?

A curated catalogue of published hyperparameter search spaces, now reaching deep neural networks

mlr3tuningspaces packages hyperparameter search spaces taken from published benchmark studies so mlr3 users can tune against a citable range instead of inventing bounds. Its release history is steady catalogue growth punctuated by compatibility bumps across the mlr3 stack. 0.7.0 adds spaces for deep neural networks from Gorishniy, Rubachev, Khrulkov and Babenko (2021) alongside mlr3 1.7.2 compatibility.

Read the full mlr3tuningspaces trajectory →

mlr3hyperband vs mlr3tuningspaces: editorial side-by-side

M
mlr3hyperband
AI-ASSISTANTS
2.5

Hyperband tuning for mlr3, now built on an asynchronous backend it treats as mandatory

◆ Current state

mlr3hyperband supplies successive-halving and Hyperband optimizers to the mlr3 tuning stack. Its recent releases are dominated by ecosystem plumbing rather than new search algorithms: a hard floor of `rush` 1.0.0, alignment with mlr3 1.7.2, and a move onto the ecosystem's new base logger. The last genuinely new optimizer was `OptimizerAsyncSuccessiveHalving` in 1.0.0.

◆ Where it's heading

The package has finished a transition from synchronous tuning to a distributed one and is now consolidating it. 1.1.1 raised the `rush` minimum to 1.0.0 and deleted every compatibility workaround for older versions, which ends the period where the async backend was optional. Logging moved the same way in 1.1.0: `bbotk`, `mlr3tuning` and `mlr3hyperband` now log through a child of a shared `mlr3` logger rather than their own.

◆ Prediction

With the compatibility layer gone, the next release is more likely to extend async optimizers than to revisit the backend, since the recent versions spent their changes on removing optionality rather than adding surface. The entries give no signal on which optimizer comes next.

M
mlr3tuningspaces
AI-ASSISTANTS
2.5

A curated catalogue of published hyperparameter search spaces, now reaching deep neural networks

◆ Current state

mlr3tuningspaces packages hyperparameter search spaces taken from published benchmark studies so mlr3 users can tune against a citable range instead of inventing bounds. Its release history is steady catalogue growth punctuated by compatibility bumps across the mlr3 stack. 0.7.0 adds spaces for deep neural networks from Gorishniy, Rubachev, Khrulkov and Babenko (2021) alongside mlr3 1.7.2 compatibility.

◆ Where it's heading

The catalogue keeps widening one paper at a time — Kühn (2018) rbv1 spaces in 0.4.0, a corrected attribution to Binder, Pfisterer and Bischl (2020) for rbv2 in the same release, and now a deep-learning set in 0.7.0. That growth is bounded by forces outside the package: 0.6.0 had to delete the `kknn` spaces outright when the underlying package left CRAN, a breaking change driven by upstream availability rather than any design decision here.

◆ Prediction

Expect further spaces from newly published benchmark papers rather than a change in what the package does, since every feature release in this window has been of that form. Whether the deep-learning spaces get extended depends on learner support elsewhere in mlr3, which these entries do not cover.

Alternatives to mlr3hyperband and mlr3tuningspaces

Other ai-assistants 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 mlr3hyperband or mlr3tuningspaces.

See all mlr3hyperband alternatives → · See all mlr3tuningspaces alternatives →

Recent activity from mlr3hyperband and mlr3tuningspaces

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

  1. 21d agomlr3hyperbandrush 1.0.0 required; old compatibility paths removed
  2. 21d agomlr3tuningspacesDeep neural network tuning spaces added
  3. 5mo agomlr3hyperbandLogging reparented under a shared mlr3 base logger
  4. 1y agomlr3hyperbandAsync successive halving lands in 1.0.0
  5. 1y agomlr3tuningspaceskknn tuning spaces removed after CRAN departure
  6. 1y agomlr3tuningspacesCompatibility with mlr3learners 0.9.0
  7. 2y agomlr3hyperbandCompatibility with bbotk and mlr3tuning 1.0.0
  8. 2y agomlr3tuningspacesCompatibility with mlr3tuning 1.0.0
  9. 2y agomlr3hyperbandCompatibility with paradox 1.0.0
  10. 2y agomlr3tuningspacesranger.rbv1 factor handling narrowed; paradox 1.0.0 support
  11. 3y agomlr3tuningspacesrbv1 search spaces added; rbv2 attribution corrected
  12. 3y agomlr3hyperbandUnloading now clears registered optimizers

Frequently asked questions

What is the difference between mlr3hyperband and mlr3tuningspaces?

Both compete on the same themes — hyperparameter-tuning, mlr3, r-package, machine-learning — within ai-assistants. mlr3hyperband and mlr3tuningspaces 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 mlr3hyperband better than mlr3tuningspaces?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3hyperband and mlr3tuningspaces 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 ai-assistants products to evaluate alongside.

What are the best alternatives to mlr3hyperband?

Top mlr3hyperband alternatives in ai-assistants are ranked by recent ship velocity. Browse the "mlr3hyperband alternatives" section above for the current picks, or visit /alternatives/mlr3hyperband for the full list with editorial commentary on each.

What are the best alternatives to mlr3tuningspaces?

Top mlr3tuningspaces alternatives in ai-assistants are ranked by recent ship velocity. Browse the "mlr3tuningspaces alternatives" section above for the current picks, or visit /alternatives/mlr3tuningspaces for the full list with editorial commentary on each.