mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
mlr3tuning vs workflowsets
A side-by-side editorial comparison of mlr3tuning and workflowsets — release velocity, themes, recent moves, and the top alternatives to consider.
mlr3tuning vs workflowsets: at a glance
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.
What is workflowsets?
workflowsets keeps widening what counts as a model worth comparing.
workflowsets holds a grid of preprocessor and model combinations and evaluates all of them under one call to workflow_map(). The releases in view widen that grid — clustering specifications via tidyclust, censored regression via an eval_time argument, case weights — and fill in the accessors around it with collect_notes(), collect_extracts() and fit_best(). The long-running pull_*() deprecation finally reached the error stage in 1.1.1.
mlr3tuning vs workflowsets: editorial side-by-side
mlr3tuning
2.5ANALYTICS
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.
workflowsets
0.0ANALYTICS
workflowsets keeps widening what counts as a model worth comparing.
◆ Current state
workflowsets holds a grid of preprocessor and model combinations and evaluates all of them under one call to workflow_map(). The releases in view widen that grid — clustering specifications via tidyclust, censored regression via an eval_time argument, case weights — and fill in the accessors around it with collect_notes(), collect_extracts() and fit_best(). The long-running pull_*() deprecation finally reached the error stage in 1.1.1.
◆ Where it's heading
The package's job is comparison, so its direction is set by what tidymodels can express: every time a new model paradigm lands elsewhere, workflowsets has to learn to rank it. Clustering was the largest of those steps because it has no outcome column to score against. Alongside that runs a slower cleanup — named-only optional arguments, type checking on inputs, informative errors when someone passes a workflow set to fit() — that reads as a package hardening after its API settled.
◆ Prediction
Expect the tailor postprocessors that workflows added in 1.3.0 to need representation here next, since a workflow set that cannot vary the postprocessor cannot compare calibration choices.
Alternatives to mlr3tuning and workflowsets
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 workflowsets.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
timetk
timetk swallowed anomalize whole, then went quiet for two years
modelbased
modelbased is turning marginal effects into a full contrast grammar
parameters
easystats' parameters package absorbs one more model class every few weeks
tidyposterior
A finished Bayesian model-comparison package in pure maintenance mode
See all mlr3tuning alternatives → · See all workflowsets alternatives →
Recent activity from mlr3tuning and workflowsets
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 18d agomlr3tuningmlr3tuning 1.6.1 stops clobbering other packages' tuner properties
- 4mo agomlr3tuningmlr3tuning 1.6.0 aligns archive column order across tuning classes
- 8mo agomlr3tuningmlr3tuning 1.5.1 tracks xgboost 3.1.2.1
- 8mo agomlr3tuningmlr3tuning 1.5.0 adds queue evaluation stages to async callbacks
- 1y agomlr3tuningmlr3tuning 1.4.0 unifies logging under a base mlr3 logger
- 1y agoworkflowsetscollect_extracts() added; pull_*() functions now error
- 1y agomlr3tuningmlr3tuning 1.3.0 adds a frozen async archive and leaner worker storage
- 2y agoworkflowsetsCensored regression evaluation; eval_time breaks positional args
- 3y agoworkflowsetsClustering models enter workflow sets via tidyclust ⚡
- 4y agoworkflowsetsCase weights supported across a workflow set
- 4y agoworkflowsetsUpdate models and recipes across a set; mixed inputs accepted
- 5y agoworkflowsetsextract_*() supersedes pull_*() across tidymodels
Frequently asked questions
What is the difference between mlr3tuning and workflowsets?
They serve adjacent needs but don't currently overlap on shipped themes. mlr3tuning is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. See the at-a-glance table above for a side-by-side breakdown of velocity, recent sparks, and editorial themes.
Is mlr3tuning better than workflowsets?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3tuning is currently shipping more aggressively (velocity 2.5 vs 0.0), with 0 editorial sparks in the last 30 days against 0. 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 workflowsets?
Top workflowsets alternatives in Analytics are ranked by recent ship velocity. Browse the "workflowsets alternatives" section above for the current picks, or visit /alternatives/workflowsets for the full list with editorial commentary on each.