tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
hardhat vs mlr3learners
A side-by-side editorial comparison of hardhat and mlr3learners — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:r-statsmachine-learning
hardhat vs mlr3learners: at a glance
What is hardhat?
hardhat keeps adding the contracts tidymodels needs next
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
What is mlr3learners?
mlr3learners spends its releases absorbing upstream churn
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
hardhat vs mlr3learners: editorial side-by-side
hardhat
0.0ANALYTICS
hardhat keeps adding the contracts tidymodels needs next
◆ Current state
hardhat is the infrastructure layer under tidymodels, defining the preprocessing and extraction contracts other packages implement. Its releases read as a list of new generics and vector classes: extract_postprocessor(), extract_fit_time(), extract_tailor(), and a quantile_pred() class for quantile-regression output. The newest release is narrow warning and missing-value handling in mold().
◆ Where it's heading
Each addition here lands ahead of a user-facing feature elsewhere in tidymodels — the postprocessor and tailor generics precede the postprocessing workflow, quantile_pred() precedes quantile prediction in parsnip. The package's own surface stays deliberately small and its cadence follows what the rest of the stack is about to need.
◆ Prediction
Expect further extraction generics and prediction-type classes as tidymodels builds out postprocessing, with hardhat's own API remaining thin.
mlr3learners
0.0ANALYTICS
mlr3learners spends its releases absorbing upstream churn
◆ Current state
mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.
◆ Where it's heading
The package's job is insulation, and the changelog shows what that costs — compatibility-only releases interleaved with small capability additions that expose more of each upstream model. The direction of travel is toward giving users access to the raw upstream objects rather than hiding them.
◆ Prediction
Expect the next release to track another upstream version bump, with incremental exposure of learner-specific fields continuing alongside.
Alternatives to hardhat and mlr3learners
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 hardhat or mlr3learners.
textrecipes
Text features finally stay sparse all the way to the model.
probably
The package that made calibration a step instead of an afterthought.
workflowsets
workflowsets keeps widening what counts as a model worth comparing.
workflows
The tidymodels pipeline grew a third stage, and it happens after the model runs.
vetiver
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
See all hardhat alternatives → · See all mlr3learners alternatives →
Recent activity from hardhat and mlr3learners
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agomlr3learnerspredict_raw across all learners, plus probit and ranger internals
- 4mo agohardhatmold() warning and quantile missing-value fixes
- 8mo agomlr3learnersxgboost 3.1.2.1 compatibility
- 9mo agomlr3learnersUncertainty estimation methods for ranger regression
- 10mo agomlr3learnersDevelopment snapshot: LDA test adjustment
- 11mo agohardhatextract_tailor() generic added
- 1y agomlr3learnerskknn learners restored after returning to CRAN
- 1y agomlr3learnerskknn learners removed after CRAN archival
- 1y agohardhatquantile_pred() class for quantile regression output
- 2y agohardhatextract_postprocessor() and extract_fit_time() generics
- 2y agohardhatDocumentation topic renamed at CRAN's request
- 3y agohardhatMulti-outcome prediction helpers and one-hot factor encoding
Frequently asked questions
What is the difference between hardhat and mlr3learners?
Both compete on the same themes — r-stats, machine-learning — within Analytics. hardhat and mlr3learners 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 hardhat better than mlr3learners?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. hardhat and mlr3learners 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 hardhat?
Top hardhat alternatives in Analytics are ranked by recent ship velocity. Browse the "hardhat alternatives" section above for the current picks, or visit /alternatives/hardhat for the full list with editorial commentary on each.
What are the best alternatives to mlr3learners?
Top mlr3learners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3learners alternatives" section above for the current picks, or visit /alternatives/mlr3learners for the full list with editorial commentary on each.