Weka vs Orange
Side-by-side comparison for macOS
Weka
7.0Collection of machine learning algorithms for data mining tasks
Orange
7.5Component-based data mining software
| Metric | Weka | Orange |
|---|---|---|
| Category | Science | Science |
| AI Score | 7.0 | 7.5 |
| 30-day Installs | 27 | 34 |
| 90-day Installs | 133 | 94 |
| 365-day Installs | 435 | 395 |
| Version | 3.8.6 | 3.40.0 |
| Auto-updates | No | No |
| Deprecated | No | No |
| GitHub Stars | 330 | 5.6K |
| GitHub Forks | 247 | 1.1K |
| Open Issues | - | 105 |
| License | — | NOASSERTION |
| Language | Java | Python |
| Last GitHub Commit | 7y ago | 1mo ago |
| First Seen | Aug 9, 2023 | Nov 8, 2013 |
Reviews
Weka
Weka is a comprehensive tool for machine learning and data mining, offering a wide range of algorithms. It's particularly beneficial for researchers and professionals in the field of data analysis. Despite its established presence, the project hasn't seen recent updates, which might be a concern.
Weka provides a collection of machine learning algorithms for data analysis and predictive modeling tasks.
Pros
- + Established and widely used in the machine learning community
- + Extensive collection of machine learning algorithms
- + User-friendly graphical interface for data analysis
- + Cross-platform compatibility
- + Supports various data formats and preprocessing techniques
Cons
- - No auto-update feature, requiring manual checks for updates
- - Last significant update was in 2019, indicating limited recent development
- - Recent licensing issues have been a point of contention
Orange
Orange is a component-based data mining software designed for interactive data analysis. It offers a user-friendly interface with a wide range of data visualization and machine learning tools, making it ideal for both beginners and experienced data analysts.
Orange allows users to create and modify data analysis workflows using visual components for data preprocessing, visualization, machine learning, and more.
Pros
- + Intuitive and interactive user interface for data analysis
- + Extensive library of built-in algorithms and visualization tools
- + Support for customization through Python scripting
Cons
- - No auto-updates feature
- - Moderate number of open issues on GitHub