Despite recent progress in artificial intelligence and machine learning, many state-of-the-art methods suffer from a lack of explainability and transparency. The ability to interpret the predictions made by machine learning models and accurately evaluate these models is crucially important. In this paper, we present an interactive visualization tool to elucidate the training process of active learning. This tool enables one to select a sample of interesting data points, view how their prediction values change at different querying stages, and thus better understand when and how active learning works. Additionally, users can utilize this tool to compare different active learning strategies simultaneously and inspect why some strategies outperform others in certain contexts. With some preliminary experiments, we demonstrate that our visualization panel has a great potential to be used in various active learning experiments and help users evaluate their models appropriately.
翻译:尽管最近在人工智能和机器学习方面取得了进展,但许多最先进的方法缺乏解释性和透明度。解释机器学习模型的预测和准确评价这些模型的能力至关重要。在本文件中,我们提出了一个互动可视化工具,以阐明积极学习的培训过程。这一工具使人们能够选择一个有趣的数据点样本,观察其预测值在不同查询阶段的变化,从而更好地了解何时和如何积极学习。此外,用户可以利用这一工具,同时比较不同的积极学习战略,并检查某些战略在某些情况下优于其他战略的原因。通过一些初步实验,我们证明我们的可视化小组有巨大的潜力用于各种积极的学习实验,并帮助用户适当评价其模型。