imbalanced-learn
Python library for handling imbalanced datasets with sampling techniques.
Pricing
Free tier
Flat rate
Adoption
→StableLicense
Open Source
Data freshness
Aging · Jun 8, 2026Overview
What is imbalanced-learn?
imbalanced-learn is a Python module that provides various under-sampling and over-sampling techniques to handle imbalanced datasets, which are common in machine learning tasks. It helps improve model performance by balancing the class distribution.
Key differentiator
“imbalanced-learn stands out by offering a wide range of sampling techniques directly compatible with scikit-learn pipelines, making it easy to integrate into existing machine learning workflows without significant overhead.”
Capability profile
Capability Radar
Honest assessment
Strengths & Weaknesses
↑ Strengths
↓ Weaknesses
The library primarily focuses on well-known methods like SMOTE and ADASYN, lacking more sophisticated or recent algorithms.
Documentation is thorough for basic usage but lacks examples and explanations for advanced configurations and edge cases.
Over-sampling techniques like SMOTE can be computationally expensive, leading to slow processing times on large datasets.
While it integrates well with scikit-learn pipelines, support for other machine learning frameworks or libraries is minimal.
Fit analysis
Who is it for?
✓ Best for
Projects dealing with highly imbalanced datasets where minority classes are critical for model performance.
Developers looking to integrate sampling techniques directly into their scikit-learn pipelines.
✕ Not a fit for
Real-time applications requiring immediate response as the library is designed for batch processing.
Scenarios where computational resources are extremely limited, given that some oversampling methods can be resource-intensive.
Cost structure
Pricing
Free Tier
Available
Open source — free to use
Starts at
$0
Model
Flat rate
Enterprise
None
Performance benchmarks
How Fast Is It?
Ecosystem
Relationships
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Next step
Get Started with imbalanced-learn
Step-by-step setup guide with code examples and common gotchas.