scikit-multiflow vs Meka
Side-by-side — pricing, strengths, tradeoffs, and fit.
scikit-multiflow
A machine learning framework for multi-output and stream data.
Meka
Open-source multi-label classification extension for Weka.
At a glance
Open SourceFree Tier
◌ Declining·From $0
Open SourceFree Tier
· Not enough data·From $0
Overview
One-liner
One-liner
A machine learning framework for multi-output and stream data.
One-liner
Open-source multi-label classification extension for Weka.
Maturity
Maturity
DecliningMaturity
Not enough dataOpen source
Open source
✓ YesOpen source
✓ YesPricing
Free tier
Free tier
✓ AvailableOpen source — free to use
Free tier
✓ AvailableOpen source — free to use
Starts at
Starts at
$0
Starts at
$0
Model
Model
Flat rate
Model
Flat rate
Enterprise
Enterprise
✕ NoneEnterprise
✕ NoneBest for
✓ Best for
Developers working on real-time data processing and analysis projects
Teams needing to handle multi-label classification in streaming environments
Projects requiring integration with existing scikit-learn workflows
✓ 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 ideal for
✕ Not for
Applications that require real-time interaction with cloud-based services
Scenarios where the local deployment of machine learning models is not feasible
✕ Not 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.
Strengths
↑ Strengths
Support for multi-label and multi-output learning
Real-time data stream processing capabilities
Integration with scikit-learn models
Evaluation metrics for streaming environments
↑ Strengths
Multi-label classification methods
Integration with Weka for machine learning tasks
Evaluation techniques specific to multi-label datasets
Weaknesses
↓ Weaknesses
Limited support for advanced stream processing techniques
Performance issues with large-scale data streams
Documentation is sparse and lacks comprehensive examples
Small community and limited third-party contributions
↓ Weaknesses
Limited language support, primarily Java-based
Complex setup process due to dependency on Weka
Limited community support and documentation
Performance issues with very large datasets