ANALYTICS
mlr3mbo picked its defaults from a benchmark study, not from taste
Alternatives · analytics tools
mlr3learners alternatives
The best mlr3learners alternatives in analytics tools, ranked by Sparkpulse's velocity_score.
Updated Aug 13, 2026
Looking for the best alternatives to mlr3learners? Sparkpulse tracks and ranks 12 alternatives in analytics tools by shipping velocity — how frequently each ships meaningful updates, verified from official changelogs. For reference, mlr3learners shipped 0 meaningful updates in the last 30 days and carries a velocity score of 0.0 out of 10 in 2026. The alternatives below are ranked the same way, so you're comparing real release momentum, not marketing claims.
About 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.
Velocity 0.0 · Last update 1h ago
Top 12 alternatives to mlr3learners
Ranked by recent ship velocity. Tap any card for the full editorial breakdown, or pivot to a head-to-head.
ANALYTICS
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
ANALYTICS
comtradr's 1.0 line is a long tail of patches against a brittle UN trade API.
ANALYTICS
bbotk is generalizing from an optimizer toolkit into an evaluation framework.
ANALYTICS
The tidymodels pipeline grew a third stage, and it happens after the model runs.
ANALYTICS
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
ANALYTICS
patchwork stopped being a ggplot composer and became a page composer.
ANALYTICS
mlr3fselect turned feature selection into an asynchronous, distributable job
ANALYTICS
lime survives on compatibility patches years after its research moment
ANALYTICS
mlr3measures is systematically retrofitting sample weights across every metric
ANALYTICS
mlr3cluster went from a handful of clusterers to covering the field
ANALYTICS
mlr3filters grows one feature-selection filter at a time
mlr3learners vs alternatives — shipping velocity at a glance
Velocity score (0–10) and meaningful releases shipped in the last 30 days, from official changelogs. Higher = shipping faster.
| Product | Velocity | Sparks · 30d | Focus areas | Latest release |
|---|---|---|---|---|
| mlr3learners (baseline) | 0.0 | 0 | mlr3machine-learningr-stats | — |
| mlr3mbo | 2.5 | 0 | bayesian-optimizationmlr3hyperparameter-tuning | mlr3mbo 1.0.0 ships benchmark-derived default settings |
| loo | 2.5 | 0 | bayesiancross-validationstan | loo_compare returns a data.frame with new uncertainty columns |
| comtradr | 2.5 | 0 | trade dataapi wrapperun comtrade | — |
| bbotk | 2.5 | 0 | black-box optimizationmlr3async execution | EvalInstance base class separates evaluation from optimization |
| workflows | 0.0 | 0 | tidymodelspipelinespostprocessing | Workflows gain a postprocessing stage via tailor |
| vetiver | 0.0 | 0 | mlopsmodel-deploymenttidymodels | — |
| patchwork | 0.0 | 0 | ggplot2compositiontables | gt tables become first-class patchwork objects |
| mlr3fselect | 0.0 | 0 | feature-selectionmlr3machine-learning | Asynchronous feature selection arrives with FSelectorAsync |
| lime | 0.0 | 0 | interpretabilitymachine-learningr-stats | — |
| mlr3measures | 0.0 | 0 | metricsmlr3machine-learning | — |
| mlr3cluster | 0.0 | 0 | clusteringmlr3machine-learning | Nine new clustering learners in one release |
| mlr3filters | 0.0 | 0 | feature-selectionmlr3machine-learning | — |
The 12 best mlr3learners alternatives, in depth
1. mlr3mbo · velocity 2.5
Mlr3mbo picked its defaults from a benchmark study, not from taste.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “mlr3mbo 1.0.0 ships benchmark-derived default settings”.
Where mlr3learners leans on mlr3, machine learning and r stats, mlr3mbo focuses on bayesian optimization, mlr3 and hyperparameter tuning.
mlr3mbo and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3mbo trajectory → · Compare mlr3learners vs mlr3mbo →
2. loo · velocity 2.5
Loo keeps rewriting the diagnostics Bayesian modellers read off model comparison.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “loo_compare returns a data.frame with new uncertainty columns”.
Where mlr3learners leans on mlr3, machine learning and r stats, loo focuses on bayesian, cross validation and stan.
loo and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
3. comtradr · velocity 2.5
Comtradr's 1.0 line is a long tail of patches against a brittle UN trade API.
Its velocity score of 2.5/10 reflects longer-term release cadence.
Where mlr3learners leans on mlr3, machine learning and r stats, comtradr focuses on trade data, api wrapper and un comtrade.
comtradr and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full comtradr trajectory → · Compare mlr3learners vs comtradr →
4. bbotk · velocity 2.5
Bbotk is generalizing from an optimizer toolkit into an evaluation framework.
Its velocity score of 2.5/10 reflects longer-term release cadence; its most recent meaningful update was “EvalInstance base class separates evaluation from optimization”.
Where mlr3learners leans on mlr3, machine learning and r stats, bbotk focuses on black box optimization, mlr3 and async execution.
bbotk and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
5. workflows · velocity 0.0
The tidymodels pipeline grew a third stage, and it happens after the model runs.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Workflows gain a postprocessing stage via tailor”.
Where mlr3learners leans on mlr3, machine learning and r stats, workflows focuses on tidymodels, pipelines and postprocessing.
workflows and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full workflows trajectory → · Compare mlr3learners vs workflows →
6. vetiver · velocity 0.0
Posit's MLOps package went quiet for two years, then came back to keep up with recipes.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where mlr3learners leans on mlr3, machine learning and r stats, vetiver focuses on mlops, model deployment and tidymodels.
vetiver and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full vetiver trajectory → · Compare mlr3learners vs vetiver →
7. patchwork · velocity 0.0
Patchwork stopped being a ggplot composer and became a page composer.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “gt tables become first-class patchwork objects”.
Where mlr3learners leans on mlr3, machine learning and r stats, patchwork focuses on ggplot2, composition and tables.
patchwork and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full patchwork trajectory → · Compare mlr3learners vs patchwork →
8. mlr3fselect · velocity 0.0
Mlr3fselect turned feature selection into an asynchronous, distributable job.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Asynchronous feature selection arrives with FSelectorAsync”.
Where mlr3learners leans on mlr3, machine learning and r stats, mlr3fselect focuses on feature selection, mlr3 and machine learning.
mlr3fselect and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3fselect trajectory → · Compare mlr3learners vs mlr3fselect →
9. lime · velocity 0.0
Lime survives on compatibility patches years after its research moment.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where mlr3learners leans on mlr3, machine learning and r stats, lime focuses on interpretability, machine learning and r stats.
lime and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
10. mlr3measures · velocity 0.0
Mlr3measures is systematically retrofitting sample weights across every metric.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where mlr3learners leans on mlr3, machine learning and r stats, mlr3measures focuses on metrics, mlr3 and machine learning.
mlr3measures and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3measures trajectory → · Compare mlr3learners vs mlr3measures →
11. mlr3cluster · velocity 0.0
Mlr3cluster went from a handful of clusterers to covering the field.
Its velocity score of 0.0/10 reflects longer-term release cadence; its most recent meaningful update was “Nine new clustering learners in one release”.
Where mlr3learners leans on mlr3, machine learning and r stats, mlr3cluster focuses on clustering, mlr3 and machine learning.
mlr3cluster and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3cluster trajectory → · Compare mlr3learners vs mlr3cluster →
12. mlr3filters · velocity 0.0
Mlr3filters grows one feature-selection filter at a time.
Its velocity score of 0.0/10 reflects longer-term release cadence.
Where mlr3learners leans on mlr3, machine learning and r stats, mlr3filters focuses on feature selection, mlr3 and machine learning.
mlr3filters and mlr3learners have shipped at a similar pace over the last 30 days, so the decision comes down to fit and feature depth.
Full mlr3filters trajectory → · Compare mlr3learners vs mlr3filters →
Frequently asked questions
What are the best alternatives to mlr3learners?
The top mlr3learners alternatives we currently track in analytics tools are mlr3mbo, loo, comtradr, bbotk, workflows, ranked by recent ship velocity.
How is this list of mlr3learners alternatives ranked?
Alternatives are ranked by Sparkpulse's velocity_score — release cadence + 30-day spark count + sector-relative ship rate.
Can I compare mlr3learners directly with one of these alternatives?
Yes — every card has a "Compare with mlr3learners" link to a side-by-side /compare page.