Meka

Open-source multi-label classification extension for Weka.

EmergingOpen SourceLow lock-in
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Pricing

Free tier

Flat rate

Adoption

→Stable

License

Open Source

Data freshness

Unverified

Overview

What is Meka?

Meka is an open-source implementation of methods for multi-label classification and evaluation, extending the capabilities of Weka. It offers advanced techniques for handling complex datasets with multiple labels per instance.

Key differentiator

“Meka stands out by providing specialized multi-label classification methods and evaluation techniques that extend the capabilities of Weka, making it ideal for complex datasets with multiple labels per instance.”

Capability profile

Capability Radar

Ease of StartEcosystemValueMaturityFlexibilityScale Ready

Honest assessment

Strengths & Weaknesses

↑ Strengths

Multi-label classification methodsmedium

Integration with Weka for machine learning tasksmedium

Evaluation techniques specific to multi-label datasetsmedium

↓ Weaknesses

Limited language support, primarily Java-basedhigh

Meka's core functionality is tightly integrated with Weka and only supports Java, limiting its accessibility to developers familiar with other languages.

Complex setup process due to dependency on Wekamedium

Setting up Meka requires a thorough understanding of both Meka and Weka configurations, which can be time-consuming and error-prone for new users.

Limited community support and documentationhigh

The open-source nature of Meka means that its documentation and user support are not as robust or readily available compared to more popular frameworks, which can hinder troubleshooting and learning.

Performance issues with very large datasetsmedium

Meka's performance may degrade when handling extremely large multi-label datasets due to its reliance on Java and the underlying Weka framework, leading to increased processing times and memory usage.

Fit analysis

Who is it for?

✓ Best for

Researchers and developers working on projects that require handling datasets with multiple labels per instance.

Teams looking to extend their machine learning capabilities in Weka with multi-label classification techniques.

✕ Not a fit for

Projects requiring real-time processing or streaming data, as Meka is designed for batch processing.

Applications needing a web-based interface; Meka operates primarily through Java libraries.

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

Next step

Get Started with Meka

Step-by-step setup guide with code examples and common gotchas.

View Setup Guide →