tidytext
Finished, widely taught, and shipping roxygen fixes.
Comparison · Analytics
bonsai vs mlr3measures
A side-by-side editorial comparison of bonsai and mlr3measures — release velocity, themes, recent moves, and the top alternatives to consider.
bonsai vs mlr3measures: at a glance
What is bonsai?
bonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
What is mlr3measures?
mlr3measures is systematically retrofitting sample weights across every metric
mlr3measures is the metric library behind mlr3. Recent releases follow two threads: adding measures — linex, pinball, Mu AUC, gmean, gpr, mcc — and retrofitting sample_weights support across the existing ones, reaching AUC and the confusion-matrix family in 1.3.0. Along the way 1.1.0 deprecated four regression measures and corrected the bias definitions.
bonsai vs mlr3measures: editorial side-by-side
bonsai
0.0ANALYTICS
bonsai keeps widening tidymodels' boosted-tree engine bench, catboost most recently
◆ Current state
bonsai exists to attach non-core tree engines to parsnip's boost_tree() and rand_forest(), and the release history reads as a steady accumulation of them: partykit, aorsf, lightgbm, and now catboost. The 0.4.x line is spent making catboost behave like a full tidymodels citizen rather than adding anything new.
◆ Where it's heading
Each engine follows the same arc — land it, then close the gaps that keep it from tuning cleanly (parameter naming, multi_predict, threading, case weights). Recent work is squarely in that second phase for catboost, with dials supplying the matching parameter objects on its own release schedule. Bug-fix density is high relative to new surface.
◆ Prediction
Expect the catboost integration to keep filling in tuning and GPU-related arguments before any further engine is added; the entries give no signal about which engine would come next.
mlr3measures
0.0ANALYTICS
mlr3measures is systematically retrofitting sample weights across every metric
◆ Current state
mlr3measures is the metric library behind mlr3. Recent releases follow two threads: adding measures — linex, pinball, Mu AUC, gmean, gpr, mcc — and retrofitting sample_weights support across the existing ones, reaching AUC and the confusion-matrix family in 1.3.0. Along the way 1.1.0 deprecated four regression measures and corrected the bias definitions.
◆ Where it's heading
The library is maturing rather than growing: weighted evaluation and observation-wise loss functions are being brought to metrics that already existed, which is what downstream weighted-resampling and per-observation analysis need. The deprecations suggest the maintainers are willing to remove measures they consider ill-defined rather than keep them for compatibility.
◆ Prediction
Expect sample_weights and observation-wise variants to reach the remaining measures that lack them.
Alternatives to bonsai and mlr3measures
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 bonsai or mlr3measures.
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 bonsai alternatives → · See all mlr3measures alternatives →
Recent activity from bonsai and mlr3measures
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agobonsaicatboost gains multi_predict() and corrected tuning parameters
- 3mo agomlr3measuresWeighted AUC and weighted confusion-matrix measures
- 8mo agomlr3measuresObservation-wise loss for bbrier and logloss
- 11mo agomlr3measuresrse, rsq, rrse and rae deprecated; bias measures corrected
- 1y agobonsaicatboost engine added to boost_tree()
- 1y agobonsailightgbm accepts sparse matrices for fit and predict
- 1y agomlr3measureslinex, pinball and Mu AUC measures added
- 2y agobonsaiaorsf fit failure in multisession workers fixed
- 2y agomlr3measuresgmean, gpr and multiclass MCC added
- 2y agobonsaiaorsf engine added; lightgbm gains dataset params and case weights
- 3y agobonsailightgbm num_leaves becomes tunable; alias arguments disallowed
- 4y agomlr3measuresObservation-wise loss functions introduced
Frequently asked questions
What is the difference between bonsai and mlr3measures?
They serve adjacent needs but don't currently overlap on shipped themes. bonsai and mlr3measures 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 bonsai better than mlr3measures?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bonsai and mlr3measures 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 bonsai?
Top bonsai alternatives in Analytics are ranked by recent ship velocity. Browse the "bonsai alternatives" section above for the current picks, or visit /alternatives/bonsai-r for the full list with editorial commentary on each.
What are the best alternatives to mlr3measures?
Top mlr3measures alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3measures alternatives" section above for the current picks, or visit /alternatives/mlr3measures for the full list with editorial commentary on each.