mlr3proba
mlr3proba is shedding weight as its survival work moves into sibling packages
Comparison · Analytics
tidypredict vs workflows
A side-by-side editorial comparison of tidypredict and workflows — release velocity, themes, recent moves, and the top alternatives to consider.
tidypredict vs workflows: 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 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.
tidypredict vs workflows: 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.
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.
Alternatives to tidypredict and workflows
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 workflows.
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 workflows alternatives →
Recent activity from tidypredict and workflows
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
- 11mo agoworkflowsWorkflows gain a postprocessing stage via tailor ⚡
- 1y agoworkflowsSparse matrices work through fit() and predict()
- 1y agotidypredicttidypredict 0.5.1 exports internals for the orbital package
- 2y agoworkflowsaugment() aligns with parsnip; censored regression supported
- 3y agoworkflowsRegister tuning generics unconditionally
- 3y agotidypredicttidypredict 0.5 hands maintenance to a new maintainer
- 3y agoworkflowsMissing parsnip extensions now error early; unsupervised specs supported
- 3y agoworkflowsMode guessing removed; silent offset handling now errors
- 4y agotidypredicttidypredict 0.4.9 relicenses to MIT and fixes SQL generation
Frequently asked questions
What is the difference between tidypredict and workflows?
They serve adjacent needs but don't currently overlap on shipped themes. tidypredict and workflows 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 workflows?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. tidypredict and workflows 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 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.