RuleKit 2大幅提升规则学习速度，支持Python和网页界面。
RuleKit 2: Faster and simpler rule learning
- 新算法与优化使部分数据集分析提速100倍
- 兼容scikit-learn，可无缝接入现有分析流程
- 提供Python包和图形化网页应用，易用性更强
规则兼具预测与解释能力。我们用于规则型数据分析的工具RuleKit已在分类、回归和生存分析中证明其有效性。本文介绍其第二版：新算法及先前算法的优化实现，显著提升计算性能，使某些数据集分析时间缩短两个数量级。RuleKit 2通过两个新组件提升可用性：符合scikit-learn标准的Python包，以及带图形界面的浏览器应用，可轻松集成到现有数据挖掘流程中。RuleKit 2可在GitHub上以GNU AGPL 3许可获取（https://github.com/adaa-polsl/RuleKit）。
原文摘要 · Abstract (English)
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)
Thank you to arXiv for use of its open access interoperability. PaperDance 不是 arXiv 官方产品；中文卡片由大模型生成，请以原文为准。