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imbalanced-learn vs rsample

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

Shared themes:resampling

imbalanced-learn vs rsample: at a glance

Featureimbalanced-learnrsample
Sectorai-assistantsai-assistants
Velocity score0.00.0
Sparks · 30d00
Top themesimbalanced-data, resampling, scikit-learn, compatibilitytidymodels, resampling, cross-validation, deprecations
Last editorial update2h ago2h ago
WebsiteVisit →Visit →

What is imbalanced-learn?

The resampling companion to scikit-learn now ships mostly to stay compatible with it.

imbalanced-learn is at 0.14.2. Four of the six releases in the window exist to track a scikit-learn version — 1.5, 1.7, 1.8 and 1.9 in turn — or NumPy 2.0. The genuine additions are thin: InstanceHardnessCV in 0.14.0 and a clearer SMOTENC error when the categorical encoder collapses categories.

Read the full imbalanced-learn trajectory →

What is rsample?

tidymodels' resampling package is retiring its old splitters for sliding windows.

rsample is at 1.3.2, a small release covering spatialsample interoperability and a soft deprecation of the lag argument on initial_time_split(). The more consequential work sits behind it: 1.3.1 added internal_calibration_split() and a calibration() accessor so tune can fit a preprocessor and a post-processor on separate parts of the analysis set, and 1.3.0 superseded rolling_origin() with the sliding_* family.

Read the full rsample trajectory →

imbalanced-learn vs rsample: editorial side-by-side

I
imbalanced-learn
AI-ASSISTANTS
0.0

The resampling companion to scikit-learn now ships mostly to stay compatible with it.

◆ Current state

imbalanced-learn is at 0.14.2. Four of the six releases in the window exist to track a scikit-learn version — 1.5, 1.7, 1.8 and 1.9 in turn — or NumPy 2.0. The genuine additions are thin: InstanceHardnessCV in 0.14.0 and a clearer SMOTENC error when the categorical encoder collapses categories.

◆ Where it's heading

The project has settled into the role of a compatibility shim with a stable sampler catalogue. Release timing is set by upstream scikit-learn, not by its own roadmap, and the deprecations queued in 0.13.0 show the surface narrowing rather than growing.

◆ Prediction

The pattern points to the next release being another scikit-learn compatibility bump, with the Pipeline check_is_fitted deprecation scheduled to become an error in 0.15.

R
rsample
AI-ASSISTANTS
0.0

tidymodels' resampling package is retiring its old splitters for sliding windows.

◆ Current state

rsample is at 1.3.2, a small release covering spatialsample interoperability and a soft deprecation of the lag argument on initial_time_split(). The more consequential work sits behind it: 1.3.1 added internal_calibration_split() and a calibration() accessor so tune can fit a preprocessor and a post-processor on separate parts of the analysis set, and 1.3.0 superseded rolling_origin() with the sliding_* family.

◆ Where it's heading

Two threads run through the window. Time-based resampling is migrating from rolling_origin() to sliding_window(), sliding_index() and sliding_period(), while validation_split() and its relatives have moved from soft deprecation to warning in favour of the three-way initial_validation_split(). Alongside that, rsample is growing infrastructure other tidymodels packages consume rather than user-facing splitters.

◆ Prediction

Given that validation_split() and friends now warn and initial_time_split()'s lag argument is soft-deprecated, the next release most likely escalates those deprecations rather than adding a resampling scheme.

Alternatives to imbalanced-learn and rsample

Other ai-assistants 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 imbalanced-learn or rsample.

See all imbalanced-learn alternatives → · See all rsample alternatives →

Recent activity from imbalanced-learn and rsample

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

  1. 2mo agoimbalanced-learnscikit-learn 1.9 compatibility and a SMOTENC error message
  2. 6mo agorsamplespatialsample interop and lag argument soft-deprecated
  3. 7mo agoimbalanced-learnscikit-learn 1.8 compatibility release
  4. 0y agoimbalanced-learnInstanceHardnessCV splits folds by sample hardness
  5. 1y agorsampleinternal_calibration_split() for post-processor fitting
  6. 1y agorsamplerolling_origin() superseded by the sliding_* family
  7. 1y agoimbalanced-learnMetadata routing for samplers and two queued deprecations
  8. 1y agoimbalanced-learnNumPy 2.0 compatibility
  9. 2y agoimbalanced-learnscikit-learn 1.5 compatibility release
  10. 2y agorsampleFixes nested_cv() with long calls
  11. 2y agorsampleThree-way train, validation and test splits
  12. 3y agorsampleStratified grouped resampling and clustering_cv()

Frequently asked questions

What is the difference between imbalanced-learn and rsample?

Both compete on the same themes — resampling — within ai-assistants. imbalanced-learn and rsample 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 imbalanced-learn better than rsample?

Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. imbalanced-learn and rsample 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 ai-assistants products to evaluate alongside.

What are the best alternatives to imbalanced-learn?

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

What are the best alternatives to rsample?

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