RuleKit 2: Faster and simpler rule learning
This work improves a software package for data analysts, offering faster and more accessible rule learning, but it is incremental as it builds on an existing tool.
The authors tackled the computational performance and usability of rule-based data analysis by presenting RuleKit 2, which reduces analysis time by up to two orders of magnitude and adds Python and browser-based interfaces for easier integration.
Rules offer an invaluable combination of predictive and descriptive capabilities. Our package for rule-based data analysis, RuleKit, has proven its effectiveness in classification, regression, and survival problems. Here we present its second version. New algorithms and optimized implementations of those previously included, significantly improved the computational performance of our suite, reducing the analysis time of some data sets by two orders of magnitude. The usability of RuleKit 2 is provided by two new components: Python package and browser application with a graphical user interface. The former complies with scikit-learn, the most popular data mining library for Python, allowing RuleKit 2 to be straightforwardly integrated into existing data analysis pipelines. RuleKit 2 is available at GitHub under GNU AGPL 3 license (https://github.com/adaa-polsl/RuleKit)