mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
workflows vs workflowsets
A side-by-side editorial comparison of workflows and workflowsets — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodelstuning
workflows vs workflowsets: at a glance
What is workflows?
The tidymodels pipeline grew a third stage, and it happens after the model runs.
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
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.
workflows vs workflowsets: editorial side-by-side
workflows
0.0ANALYTICS
The tidymodels pipeline grew a third stage, and it happens after the model runs.
◆ Current state
workflows bundles a preprocessor and a model into one object that tidymodels can fit, tune and extract from. Version 1.3.0 added a post stage backed by the tailor package, wired through every generic a workflow supports — augment, tidy, tunable, tune_args, required_pkgs and parameter extraction. Version 1.2.0 added sparse data support so fit() and predict() accept dgCMatrix and sparse tibbles. Earlier releases in view are boundary tightening: erroring on unknown model modes, on trained recipes, and on silently ignored formula offsets.
◆ Where it's heading
The object is filling out into a complete pipeline description rather than a preprocessing-plus-model pair. Postprocessing is the structural addition — calibration and threshold selection were previously done by hand after prediction, outside anything tidymodels could tune or record — and the fact that it arrived integrated with tunable() and tune_args() rather than as a standalone step is the point. The rest of the arc is the steady tidymodels habit of converting silent guesses into errors.
◆ Prediction
Expect tailor postprocessors to spread through tune and workflowsets next, since the parameter and tuning generics were wired up first, and expect sparse support to extend to more engines after lightgbm.
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 workflows 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 workflows or workflowsets.
mlr3viz
mlr3viz keeps the ecosystem's plots working while the plots themselves move out
mlr3tuning
mlr3tuning is rebuilding its async machinery under a stable public surface
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
See all workflows alternatives → · See all workflowsets alternatives →
Recent activity from workflows and workflowsets
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 1y agoworkflowsetscollect_extracts() added; pull_*() functions now error
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 2y agoworkflowsetsCensored regression evaluation; eval_time breaks positional args
- 2y agoworkflowsaugment() aligns with parsnip; censored regression supported
- 3y agoworkflowsetsClustering models enter workflow sets via tidyclust ⚡
- 3y agoworkflowsRegister tuning generics unconditionally
- 3y agoworkflowsMissing parsnip extensions now error early; unsupervised specs supported
- 3y agoworkflowsMode guessing removed; silent offset handling now errors
- 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 workflows and workflowsets?
Both compete on the same themes — tidymodels, tuning — within Analytics. workflows and workflowsets are shipping at a similar cadence (velocity 0.0 vs 0.0, 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 workflows better than workflowsets?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. workflows and workflowsets are shipping at a similar cadence (velocity 0.0 vs 0.0, 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 workflows?
Top workflows alternatives in Analytics are ranked by recent ship velocity. Browse the "workflows alternatives" section above for the current picks, or visit /alternatives/workflows-r 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.