imbalanced-learn

What it is

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.

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.

When it fits — and when it doesn’t

Known weaknesses

Each one links to the public issue, changelog or docs page it came from, where we have one.

1. 01

   Limited support for advanced techniques beyond SMOTE and ADASYN

   The library primarily focuses on well-known methods like SMOTE and ADASYN, lacking more sophisticated or recent algorithms.

2. 02

   Poor documentation for complex use cases

   Documentation is thorough for basic usage but lacks examples and explanations for advanced configurations and edge cases.

3. 03

   Performance issues with large datasets

   Over-sampling techniques like SMOTE can be computationally expensive, leading to slow processing times on large datasets.

4. 04

   Limited integration support outside of scikit-learn ecosystem

   While it integrates well with scikit-learn pipelines, support for other machine learning frameworks or libraries is minimal.

What it does well

• Various over-sampling techniques like SMOTE, ADASYN

• Under-sampling methods including Random Under-Sampling and Tomek Links

• Pipeline integration with scikit-learn for seamless use in ML workflows

Pricing

Free tier

YesOpen source — free to use

Paid from

$0

Model

Flat rate

Enterprise plan

No

Pricing not independently confirmed — check the vendor’s page.