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Comparison · Analytics
mlr3measures vs see
A side-by-side editorial comparison of mlr3measures and see — release velocity, themes, recent moves, and the top alternatives to consider.
mlr3measures vs see: at a glance
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.
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.
mlr3measures vs see: editorial side-by-side
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.
see
0.0ANALYTICS
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 mlr3measures 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 mlr3measures or see.
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 mlr3measures alternatives → · See all see alternatives →
Recent activity from mlr3measures and see
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 1mo agoseesee 0.14.1 adds plots for prior checks and grouped means
- 2mo agoseesee 0.14.0 renders factor loadings as node-edge graphs
- 3mo agomlr3measuresWeighted AUC and weighted confusion-matrix measures
- 6mo agoseesee 0.13.0 fixes reversed plot sorting, adds theme arguments
- 8mo agomlr3measuresObservation-wise loss for bbrier and logloss
- 11mo agoseesee 0.12.0 extends normality checks to psych factor models
- 11mo agomlr3measuresrse, rsq, rrse and rae deprecated; bias measures corrected
- 1y agoseesee 0.11.0 scales theme elements with base_size
- 1y agoseesee 0.10.0 plots random-effect group levels for mixed models
- 1y agomlr3measureslinex, pinball and Mu AUC measures added
- 2y agomlr3measuresgmean, gpr and multiclass MCC added
- 4y agomlr3measuresObservation-wise loss functions introduced
Frequently asked questions
What is the difference between mlr3measures and see?
They serve adjacent needs but don't currently overlap on shipped themes. mlr3measures 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 mlr3measures better than see?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3measures 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 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.
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.