AstronomicAL is a human-in-the-loop interactive labelling and training dashboard that allows users to create reliable datasets and robust classifiers using active learning. This technique prioritises data that offer high information gain, leading to improved performance using substantially less data. The system allows users to visualise and integrate data from different sources and deal with incorrect or missing labels and imbalanced class sizes. AstronomicAL enables experts to visualise domain-specific plots and key information relating both to broader context and details of a point of interest drawn from a variety of data sources, ensuring reliable labels. In addition, AstronomicAL provides functionality to explore all aspects of the training process, including custom models and query strategies. This makes the software a tool for experimenting with both domain-specific classifications and more general-purpose machine learning strategies. We illustrate using the system with an astronomical dataset due to the field's immediate need; however, AstronomicAL has been designed for datasets from any discipline. Finally, by exporting a simple configuration file, entire layouts, models, and assigned labels can be shared with the community. This allows for complete transparency and ensures that the process of reproducing results is effortless

中文翻译：

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AstronomicAL：一个交互式仪表板，用于使用主动学习对数据进行可视化、集成和分类

AstronomicAL 是一个人在环交互式标记和训练仪表板，允许用户使用主动学习创建可靠的数据集和强大的分类器。该技术优先考虑提供高信息增益的数据，从而使用显着减少的数据提高性能。该系统允许用户可视化和整合来自不同来源的数据，并处理不正确或缺失的标签和不平衡的类大小。AstronomicAL 使专家能够将特定领域的图和关键信息可视化，这些信息与从各种数据源中提取的更广泛的背景和兴趣点的细节相关，从而确保可靠的标签。此外，AstronomicAL 提供了探索训练过程各个方面的功能，包括自定义模型和查询策略。这使得该软件成为试验特定领域分类和更通用机器学习策略的工具。由于该领域的迫切需要，我们将使用带有天文数据集的系统进行说明；然而，AstronomicAL 是为来自任何学科的数据集而设计的。最后，通过导出一个简单的配置文件，可以与社区共享整个布局、模型和分配的标签。这允许完全透明并确保再现结果的过程毫不费力 通过导出一个简单的配置文件，可以与社区共享整个布局、模型和分配的标签。这允许完全透明并确保再现结果的过程毫不费力 通过导出一个简单的配置文件，可以与社区共享整个布局、模型和分配的标签。这允许完全透明并确保再现结果的过程毫不费力