Summary of Sdrdpy: An Application to Graphically Visualize the Knowledge Obtained with Supervised Descriptive Rule Algorithms, by M.a. Padilla-rascon et al.
SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms
by M.A. Padilla-Rascon, P. Gonzalez, C.J. Carmona
First submitted to arxiv on: 31 Jan 2024
Categories
- Main: Artificial Intelligence (cs.AI)
- Secondary: None
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| Summary difficulty | Written by | Summary |
|---|---|---|
| High | Paper authors | High Difficulty Summary Read the original abstract here |
| Medium | GrooveSquid.com (original content) | Medium Difficulty Summary This paper introduces SDRDPy, a desktop application that enables experts to visualize and analyze the output of supervised descriptive rule discovery algorithms. The tool provides an intuitive graphical and tabular representation of extracted knowledge, allowing users to easily identify relevant information, relationships between rules and data, and quality measures for each rule, regardless of the original algorithm’s execution environment. By presenting all this information in a user-friendly format, SDRDPy facilitates expert analysis and report exportation in various formats. |
| Low | GrooveSquid.com (original content) | Low Difficulty Summary This paper makes it easy for experts to understand what machine learning algorithms are saying about your data! They built a special program called SDRDPy that shows you important things like which rules are good or bad, how they relate to each other and the data, and what makes them work well. It’s all presented in a simple and clear way so you can easily make sense of it. You can even print out reports in different formats. |
Keywords
» Artificial intelligence » Machine learning » Supervised
