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mlr3learners vs mlr3measures

A side-by-side editorial comparison of mlr3learners and mlr3measures — release velocity, themes, recent moves, and the top alternatives to consider.

Shared themes:mlr3machine-learningr-stats

mlr3learners vs mlr3measures: at a glance

Featuremlr3learnersmlr3measures
SectorAnalyticsAnalytics
Velocity score0.00.0
Sparks · 30d00
Top themesmlr3, machine-learning, r-stats, learnersmetrics, mlr3, machine-learning, r-stats
Last editorial update1h ago1h ago
WebsiteVisit →Visit →

What is mlr3learners?

mlr3learners spends its releases absorbing upstream churn

mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.

Read the full mlr3learners trajectory →

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.

Read the full mlr3measures trajectory →

mlr3learners vs mlr3measures: editorial side-by-side

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mlr3learners
ANALYTICS
0.0

mlr3learners spends its releases absorbing upstream churn

◆ Current state

mlr3learners wraps the standard model packages — ranger, xgboost, glmnet, kknn — for mlr3. Its recent history is dominated by upstream events rather than its own plans: kknn was pulled from CRAN and its learners removed in 0.11.0, then restored in 0.12.0 when the package returned. The newest release absorbs glmnet 5.0 while adding a predict_raw flag across all learners and probit support to logistic regression.

◆ Where it's heading

The package's job is insulation, and the changelog shows what that costs — compatibility-only releases interleaved with small capability additions that expose more of each upstream model. The direction of travel is toward giving users access to the raw upstream objects rather than hiding them.

◆ Prediction

Expect the next release to track another upstream version bump, with incremental exposure of learner-specific fields continuing alongside.

M
mlr3measures
ANALYTICS
0.0

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.

Alternatives to mlr3learners and mlr3measures

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 mlr3learners or mlr3measures.

See all mlr3learners alternatives → · See all mlr3measures alternatives →

Recent activity from mlr3learners and mlr3measures

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

  1. 2mo agomlr3learnerspredict_raw across all learners, plus probit and ranger internals
  2. 3mo agomlr3measuresWeighted AUC and weighted confusion-matrix measures
  3. 8mo agomlr3learnersxgboost 3.1.2.1 compatibility
  4. 8mo agomlr3measuresObservation-wise loss for bbrier and logloss
  5. 9mo agomlr3learnersUncertainty estimation methods for ranger regression
  6. 10mo agomlr3learnersDevelopment snapshot: LDA test adjustment
  7. 11mo agomlr3measuresrse, rsq, rrse and rae deprecated; bias measures corrected
  8. 1y agomlr3learnerskknn learners restored after returning to CRAN
  9. 1y agomlr3learnerskknn learners removed after CRAN archival
  10. 1y agomlr3measureslinex, pinball and Mu AUC measures added
  11. 2y agomlr3measuresgmean, gpr and multiclass MCC added
  12. 4y agomlr3measuresObservation-wise loss functions introduced

Frequently asked questions

What is the difference between mlr3learners and mlr3measures?

Both compete on the same themes — mlr3, machine-learning, r-stats — within Analytics. mlr3learners and mlr3measures 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 mlr3learners better than mlr3measures?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. mlr3learners and mlr3measures 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 mlr3learners?

Top mlr3learners alternatives in Analytics are ranked by recent ship velocity. Browse the "mlr3learners alternatives" section above for the current picks, or visit /alternatives/mlr3learners for the full list with editorial commentary on each.

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.