mlr3mbo
mlr3mbo picked its defaults from a benchmark study, not from taste
Comparison · Analytics
desirability2 vs embed
A side-by-side editorial comparison of desirability2 and embed — release velocity, themes, recent moves, and the top alternatives to consider.
Shared themes:tidymodels
desirability2 vs embed: 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 embed?
embed keeps adding encoding steps while shedding its deep-learning dependencies
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
desirability2 vs embed: 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.
embed
0.0ANALYTICS
embed keeps adding encoding steps while shedding its deep-learning dependencies
◆ Current state
embed supplies recipes steps that turn categorical predictors into numeric representations — likelihood encoding, UMAP projection, string-distance collapsing. The 1.1.x line made UMAP arguments tunable and moved keras and tensorflow out of hard dependencies; 1.2.0 added analytical likelihood encoding with partial pooling and retired step_feature_hash() in favor of textrecipes.
◆ Where it's heading
Two quiet directions run through these releases. One is making the steps tunable rather than fixed, so they participate properly in tidymodels grids. The other is boundary maintenance: heavy dependencies pushed to Suggests, overlapping steps handed to the package that owns them. Recent releases are thin and fix-driven.
◆ Prediction
Expect further consolidation with textrecipes over which package owns which encoding step, and continued upkeep against xgboost and uwot releases rather than new step families.
Alternatives to desirability2 and embed
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 embed.
loo
loo keeps rewriting the diagnostics Bayesian modellers read off model comparison
mlr3fselect
mlr3fselect turned feature selection into an asynchronous, distributable job
lime
lime survives on compatibility patches years after its research moment
mlr3measures
mlr3measures is systematically retrofitting sample weights across every metric
mlr3cluster
mlr3cluster went from a handful of clusterers to covering the field
See all desirability2 alternatives → · See all embed alternatives →
Recent activity from desirability2 and embed
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 6mo agoembedstep_umap() zero-component bug fixed
- 8mo agoembedCompatibility with all xgboost versions
- 11mo agoembedstep_lencode() adds analytical likelihood encoding with pooling
- 11mo agodesirability2make_desirability_cols() exported; data-driven limits on by default
- 1y agodesirability2Desirability-based model selection added for tune ⚡
- 1y agoembedUMAP initial and target_weight become tunable
- 2y agoembedkeras and tensorflow moved to Suggests
- 2y agoembedstep_collapse_stringdist() returns factors
- 3y agodesirability2NEWS.md added to track package changes
Frequently asked questions
What is the difference between desirability2 and embed?
Both compete on the same themes — tidymodels — within Analytics. desirability2 and embed 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 embed?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. desirability2 and embed 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 embed?
Top embed alternatives in Analytics are ranked by recent ship velocity. Browse the "embed alternatives" section above for the current picks, or visit /alternatives/embed for the full list with editorial commentary on each.