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Comparison · Analytics
desirability2 vs mlr3fselect
A side-by-side editorial comparison of desirability2 and mlr3fselect — release velocity, themes, recent moves, and the top alternatives to consider.
desirability2 vs mlr3fselect: at a glance
What is desirability2?
desirability2 is making multi-metric model selection a first-class tidymodels step.
desirability2 implements desirability functions, which map several metrics onto a common 0-1 scale so they can be combined into a single objective. The package is young: three releases, the first of which only added a NEWS file. Its substance arrived in 0.1.0 with hooks into tidymodels' tune package.
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.
desirability2 vs mlr3fselect: editorial side-by-side
desirability2
0.0ANALYTICS
desirability2 is making multi-metric model selection a first-class tidymodels step.
◆ Current state
desirability2 implements desirability functions, which map several metrics onto a common 0-1 scale so they can be combined into a single objective. The package is young: three releases, the first of which only added a NEWS file. Its substance arrived in 0.1.0 with hooks into tidymodels' tune package.
◆ Where it's heading
The direction is integration rather than standalone use. Version 0.1.0 added select_best_desirability() and show_best_desirability() to resolve a tuning run against several metrics at once; 0.2.0 exported make_desirability_cols() so other packages can build on it and made data-driven limits the default, removing the need to state ranges by hand. Both releases move work from the user into the package.
◆ Prediction
The exported helper and the developer-facing desirability() API point to adoption by other tidymodels packages as the next step rather than new functionality here.
mlr3fselect
0.0ANALYTICS
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 desirability2 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 desirability2 or mlr3fselect.
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 desirability2 alternatives → · See all mlr3fselect alternatives →
Recent activity from desirability2 and mlr3fselect
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agomlr3fselectEmpty-selection rows removable from ensemble results
- 8mo agomlr3fselectFaster objective evaluation and always_included roles
- 11mo agodesirability2make_desirability_cols() exported; data-driven limits on by default
- 1y agomlr3fselectAsynchronous feature selection arrives with FSelectorAsync ⚡
- 1y agodesirability2Desirability-based model selection added for tune ⚡
- 1y agomlr3fselectEmbedded ensemble selection and result combination
- 1y agomlr3fselectInternal tuning callback added
- 1y agomlr3fselectmlr3 0.21.0 compatibility and archive slimming
- 3y agodesirability2NEWS.md added to track package changes
Frequently asked questions
What is the difference between desirability2 and mlr3fselect?
They serve adjacent needs but don't currently overlap on shipped themes. desirability2 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 desirability2 better than mlr3fselect?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. desirability2 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 desirability2?
Top desirability2 alternatives in Analytics are ranked by recent ship velocity. Browse the "desirability2 alternatives" section above for the current picks, or visit /alternatives/desirability2 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.