mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
tidypredict vs workflowsets
A side-by-side editorial comparison of tidypredict and workflowsets — release velocity, themes, recent moves, and the top alternatives to consider.
tidypredict vs workflowsets: at a glance
What is tidypredict?
tidypredict now translates the gradient-boosting libraries people actually deploy
tidypredict converts fitted R models into SQL and dplyr expressions so predictions can run inside a database instead of in R. The 1.1.0 release added rpart, CatBoost, and LightGBM, with full objective and tree-type coverage for the boosted models. That followed 1.0.0, which broke random-forest output into a single formula, added glmnet, and cut fit-translation time for xgboost, partykit, and ranger.
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.
tidypredict vs workflowsets: editorial side-by-side
tidypredict
0.0ANALYTICS
tidypredict now translates the gradient-boosting libraries people actually deploy
◆ Current state
tidypredict converts fitted R models into SQL and dplyr expressions so predictions can run inside a database instead of in R. The 1.1.0 release added rpart, CatBoost, and LightGBM, with full objective and tree-type coverage for the boosted models. That followed 1.0.0, which broke random-forest output into a single formula, added glmnet, and cut fit-translation time for xgboost, partykit, and ranger.
◆ Where it's heading
The package's value scales directly with how many model types it can translate, and the recent work has concentrated on the tree ensembles that dominate tabular modelling in practice. Coverage now extends past what parsnip wraps, since raw CatBoost models are supported alongside parsnip and bonsai ones with an explicit escape hatch for categorical features. Performance work on the translation step suggests the models being converted have grown large enough for that to matter.
◆ Prediction
With the major boosting libraries covered, the remaining gap is what happens to preprocessing, so tighter integration with recipes or orbital for translating whole workflows is the natural next step.
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 tidypredict 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 tidypredict 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 tidypredict alternatives → · See all workflowsets alternatives →
Recent activity from tidypredict and workflowsets
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 5mo agotidypredicttidypredict 1.1.0 adds CatBoost, LightGBM, and rpart support ⚡
- 8mo agotidypredicttidypredict 1.0.1 fixes base_score extraction for xgboost 3
- 8mo agotidypredicttidypredict 1.0.0 unifies random forest output and adds glmnet
- 1y agoworkflowsetscollect_extracts() added; pull_*() functions now error
- 1y agotidypredicttidypredict 0.5.1 exports internals for the orbital package
- 2y agoworkflowsetsCensored regression evaluation; eval_time breaks positional args
- 3y agoworkflowsetsClustering models enter workflow sets via tidyclust ⚡
- 3y agotidypredicttidypredict 0.5 hands maintenance to a new maintainer
- 4y agoworkflowsetsCase weights supported across a workflow set
- 4y agotidypredicttidypredict 0.4.9 relicenses to MIT and fixes SQL generation
- 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 tidypredict and workflowsets?
They serve adjacent needs but don't currently overlap on shipped themes. tidypredict 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 tidypredict better than workflowsets?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tidypredict 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 tidypredict?
Top tidypredict alternatives in Analytics are ranked by recent ship velocity. Browse the "tidypredict alternatives" section above for the current picks, or visit /alternatives/tidypredict 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.