imbalanced-ensemble vs imbalanced-learn

Side-by-side — pricing, strengths, tradeoffs, and fit.

imbalanced-ensemble

Python toolbox for ensemble learning on imbalanced datasets.

imbalanced-learn

Python library for handling imbalanced datasets with sampling techniques.

At a glance

Open SourceFree Tier
● Established·From $0
Open SourceFree Tier
● Established·From $0

Overview

One-liner

Python toolbox for ensemble learning on imbalanced datasets.

One-liner

Python library for handling imbalanced datasets with sampling techniques.

Maturity

Established

Maturity

Established

Open source

✓ Yes

Open source

✓ Yes

Pricing

Free tier

✓ Available

Open source — free to use

Free tier

✓ Available

Open source — free to use

Starts at

$0

Starts at

$0

Model

Flat rate

Model

Flat rate

Enterprise

✕ None

Enterprise

✕ None

Best for

✓ Best for

Researchers and practitioners working with highly imbalanced datasets who need robust ensemble learning solutions.
Teams that require a comprehensive toolkit for evaluating different ensemble techniques on class-imbalanced data.

✓ 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 ideal for

✕ Not for

Projects requiring real-time processing of streaming data, as this library is designed for batch processing.
Applications where the primary focus is not on handling imbalanced datasets.

✕ Not 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.

Strengths

↑ Strengths

Supports quick implementation and evaluation of ensemble methods for imbalanced datasets.
Includes tools for visualization and modification of ensemble algorithms.
Out-of-the-box support for multi-class imbalanced classification.

↑ Strengths

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

Weaknesses

↓ Weaknesses

Limited language support
Small community and limited third-party integrations
Documentation lacks depth for advanced use cases

↓ Weaknesses

Limited support for advanced techniques beyond SMOTE and ADASYN
Poor documentation for complex use cases
Performance issues with large datasets
Limited integration support outside of scikit-learn ecosystem
Full imbalanced-ensemble profile →Full imbalanced-learn profile →