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bonsai vs mlr3fselect

A side-by-side editorial comparison of bonsai and mlr3fselect — release velocity, themes, recent moves, and the top alternatives to consider.

bonsai vs mlr3fselect: at a glance

Featurebonsaimlr3fselect
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themestidymodels, gradient-boosting, engines, lightgbmfeature-selection, mlr3, machine-learning, r-stats
Last editorial update4h ago1h ago
WebsiteVisit →Visit →

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.

Read the full bonsai trajectory →

What is mlr3fselect?

mlr3fselect turned feature selection into an asynchronous, distributable job

mlr3fselect runs feature selection for mlr3. The defining change in this window is 1.4.0, which introduced FSelectorAsync and the asynchronous instance classes, letting searches run without a synchronous batch loop. Around it sit ensemble feature selection work — fastVoteR ranking, embedded ensemble selection, result combination — and performance work on objective evaluation.

Read the full mlr3fselect trajectory →

bonsai vs mlr3fselect: editorial side-by-side

B
bonsai
ANALYTICS
0.0

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.

M
mlr3fselect
ANALYTICS
0.0

mlr3fselect turned feature selection into an asynchronous, distributable job

◆ Current state

mlr3fselect runs feature selection for mlr3. The defining change in this window is 1.4.0, which introduced FSelectorAsync and the asynchronous instance classes, letting searches run without a synchronous batch loop. Around it sit ensemble feature selection work — fastVoteR ranking, embedded ensemble selection, result combination — and performance work on objective evaluation.

◆ Where it's heading

Two threads run in parallel: scaling the search itself through async execution and the rush backend, and making ensemble selection results easier to analyse via Pareto fronts, knee points and now removal of empty result rows. The rush backward-compatibility shim was dropped in 1.6.0, so the async path is now the assumed one.

◆ Prediction

Expect the ensemble result API to keep gaining analysis helpers, with async execution treated as the default rather than an option.

Alternatives to bonsai and mlr3fselect

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 mlr3fselect.

See all bonsai alternatives → · See all mlr3fselect alternatives →

Recent activity from bonsai and mlr3fselect

Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.

  1. 2mo agomlr3fselectEmpty-selection rows removable from ensemble results
  2. 2mo agobonsaicatboost gains multi_predict() and corrected tuning parameters
  3. 8mo agomlr3fselectFaster objective evaluation and always_included roles
  4. 1y agomlr3fselectAsynchronous feature selection arrives with FSelectorAsync
  5. 1y agobonsaicatboost engine added to boost_tree()
  6. 1y agobonsailightgbm accepts sparse matrices for fit and predict
  7. 1y agomlr3fselectEmbedded ensemble selection and result combination
  8. 1y agomlr3fselectInternal tuning callback added
  9. 1y agomlr3fselectmlr3 0.21.0 compatibility and archive slimming
  10. 2y agobonsaiaorsf fit failure in multisession workers fixed
  11. 2y agobonsaiaorsf engine added; lightgbm gains dataset params and case weights
  12. 3y agobonsailightgbm num_leaves becomes tunable; alias arguments disallowed

Frequently asked questions

What is the difference between bonsai and mlr3fselect?

They serve adjacent needs but don't currently overlap on shipped themes. bonsai and mlr3fselect 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 mlr3fselect?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. bonsai and mlr3fselect 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 mlr3fselect?

Top mlr3fselect alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3fselect alternatives" section above for the current picks, or visit /alternatives/mlr3fselect for the full list with editorial commentary on each.