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Comparison · Analytics

mlr3fselect vs see

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

mlr3fselect vs see: at a glance

Featuremlr3fselectsee
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesfeature-selection, mlr3, machine-learning, r-statsr, easystats, data-visualization, ggplot2
Last editorial update1h ago5h ago
WebsiteVisit →Visit →

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 →

What is see?

see grows wherever easystats adds a diagnostic, one plot method at a time.

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

Read the full see trajectory →

mlr3fselect vs see: editorial side-by-side

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.

S
see
ANALYTICS
0.0

see grows wherever easystats adds a diagnostic, one plot method at a time.

◆ Current state

see is the visualization layer for the easystats ecosystem, supplying plot() methods for performance, parameters and datawizard objects. Each release adds methods for whatever those packages shipped — prior predictive checks, DAG diagrams, factor-analysis graphs — alongside steady theme and geom refinement.

◆ Where it's heading

Growth here is downstream-driven rather than self-directed: see expands to cover new diagnostics as easystats produces them. Running alongside that is a sustained investment in presentation control — theme arguments on plot methods, elements that scale with base_size — which suits users embedding these plots in documents rather than glancing at them interactively.

◆ Prediction

Expect new plot methods to keep arriving in step with performance and parameters releases, with continued theming work rather than any change in the package's scope.

Alternatives to mlr3fselect and see

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 mlr3fselect or see.

See all mlr3fselect alternatives → · See all see alternatives →

Recent activity from mlr3fselect and see

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

  1. 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
  2. 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
  3. 2mo agomlr3fselectEmpty-selection rows removable from ensemble results
  4. 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
  5. 8mo agomlr3fselectFaster objective evaluation and always_included roles
  6. 11mo agoseesee 0.12.0 extends normality checks to psych factor models
  7. 1y agomlr3fselectAsynchronous feature selection arrives with FSelectorAsync
  8. 1y agoseesee 0.11.0 scales theme elements with base_size
  9. 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
  10. 1y agomlr3fselectEmbedded ensemble selection and result combination
  11. 1y agomlr3fselectInternal tuning callback added
  12. 1y agomlr3fselectmlr3 0.21.0 compatibility and archive slimming

Frequently asked questions

What is the difference between mlr3fselect and see?

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

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

What are the best alternatives to see?

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