rsample
tidymodels' resampling package is retiring its old splitters for sliding windows.
Comparison · ai-assistants
imbalanced-learn vs parsnip
A side-by-side editorial comparison of imbalanced-learn and parsnip — release velocity, themes, recent moves, and the top alternatives to consider.
imbalanced-learn vs parsnip: at a glance
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.
What is parsnip?
parsnip added a whole new regression type, then wired R models to JAX and PyTorch
The package is expanding what tidymodels can express. Version 1.5.0 introduced ordinal_reg() as a new model type with three engines and its own link parameters, and added xgboost and qrnn engines for quantile regression. Version 1.6.0 followed with a keras3 engine for four model types, reaching Keras v3's TensorFlow, JAX and PyTorch backends. Around those, releases have been tuning-parameter range adjustments and engine-specific fixes.
imbalanced-learn vs parsnip: editorial side-by-side
imbalanced-learn
0.0AI-ASSISTANTS
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.
parsnip
0.0AI-ASSISTANTS
parsnip added a whole new regression type, then wired R models to JAX and PyTorch
◆ Current state
The package is expanding what tidymodels can express. Version 1.5.0 introduced ordinal_reg() as a new model type with three engines and its own link parameters, and added xgboost and qrnn engines for quantile regression. Version 1.6.0 followed with a keras3 engine for four model types, reaching Keras v3's TensorFlow, JAX and PyTorch backends. Around those, releases have been tuning-parameter range adjustments and engine-specific fixes.
◆ Where it's heading
Growth is happening on two axes: new modelling tasks that previously had no unified interface, and new engines behind tasks that already did. Both push in the same direction - a modeller specifies the model once and swaps the computational backend underneath, which is the whole premise parsnip is built on. The defunct surv_reg() shows old spellings being retired as that surface settles.
◆ Prediction
Expect further engines behind ordinal_reg() and quantile regression now that both have a home, and continued retirement of deprecated function names. The keras3 engine's multi-backend design is the obvious candidate to spread to more model types.
Alternatives to imbalanced-learn and parsnip
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 parsnip.
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See all imbalanced-learn alternatives → · See all parsnip alternatives →
Recent activity from imbalanced-learn and parsnip
Latest ship moves from both products, interleaved chronologically. ⚡ = editorial spark.
- 2mo agoimbalanced-learnscikit-learn 1.9 compatibility and a SMOTENC error message
- 3mo agoparsnipkeras3 engine brings JAX and PyTorch backends to four models
- 4mo agoparsnipparsnip adds ordinal_reg() as a first-class model type ⚡
- 7mo agoparsnipxgboost prediction fix when trees matches model size
- 7mo agoimbalanced-learnscikit-learn 1.8 compatibility release
- 8mo agoparsnipGeneralized random forests enabled; surv_reg() made defunct
- 11mo agoparsnipbrulee tuning parameter configuration fixes
- 0y agoimbalanced-learnInstanceHardnessCV splits folds by sample hardness
- 1y agoparsnipSwitch to base R pipe for CRAN compliance
- 1y agoimbalanced-learnMetadata routing for samplers and two queued deprecations
- 1y agoimbalanced-learnNumPy 2.0 compatibility
- 2y agoimbalanced-learnscikit-learn 1.5 compatibility release
Frequently asked questions
What is the difference between imbalanced-learn and parsnip?
They serve adjacent needs but don't currently overlap on shipped themes. imbalanced-learn and parsnip 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 parsnip?
Sparkpulse doesn't pick a winner — we score release velocity, not feature parity. imbalanced-learn and parsnip 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 parsnip?
Top parsnip alternatives in ai-assistants are ranked by recent ship velocity. Browse the "parsnip alternatives" section above for the current picks, or visit /alternatives/parsnip for the full list with editorial commentary on each.