SDRDPy: An application to graphically visualize the knowledge obtained with supervised descriptive rule algorithms
This tool addresses the need for better interpretability in rule-based machine learning for domain experts, though it is incremental as it builds on existing algorithms without introducing new methods.
The authors tackled the problem of interpreting knowledge from supervised descriptive rule discovery algorithms by developing SDRDPy, a desktop application that provides intuitive graphical and tabular visualizations, enabling experts to analyze data, rules, and quality measures in a user-friendly interface.
SDRDPy is a desktop application that allows experts an intuitive graphic and tabular representation of the knowledge extracted by any supervised descriptive rule discovery algorithm. The application is able to provide an analysis of the data showing the relevant information of the data set and the relationship between the rules, data and the quality measures associated for each rule regardless of the tool where algorithm has been executed. All of the information is presented in a user-friendly application in order to facilitate expert analysis and also the exportation of reports in different formats.