regAL让科研人员用最少实验数据高效训练回归模型。
regAL: Python Package for Active Learning of Regression Problems
- 提供多种主动学习策略，自动选择最有价值的数据点
- 仅需输入原始数据即可启动，支持灵活定制和结果分析
- 适合化学、材料、医学等实验成本高的领域研究者
越来越多的研究领域依赖机器学习加速发现并节省资源。然而，机器学习模型通常需要大量实验或计算数据，而在（生物）化学、材料科学或医学等领域，这类数据往往稀缺且获取成本高昂。为突破这一瓶颈，主动学习方法被用于在最少的计算或实验数据下构建高性能模型。其核心思想是通过评估模型对应用领域中某些区域的知识水平，来指导训练集的选择。尽管主动学习在分类问题（离散输出）中已有广泛研究，但针对回归问题（连续输出）的工作仍较少。本文提出 Python 工具包 regAL，使用户能够评估不同主动学习策略在回归任务中的表现。该工具只需提供目标数据集即可运行，同时支持多种自定义选项与可视化洞察，旨在帮助任何希望在其特定问题中开展并理解主动学习的研究者。
原文摘要 · Abstract (English)
Increasingly more research areas rely on machine learning methods to accelerate discovery while saving resources. Machine learning models, however, usually require large datasets of experimental or computational results, which in certain fields, such as (bio)chemistry, materials science, or medicine, are rarely given and often prohibitively expensive to obtain. To bypass that obstacle, active learning methods are employed to develop machine learning models with a desired performance while requiring the least possible number of computational or experimental results from the domain of application. For this purpose, the model's knowledge about certain regions of the application domain is estimated to guide the choice of the model's training set. Although active learning is widely studied for classification problems (discrete outcomes), comparatively few works handle this method for regression problems (continuous outcomes). In this work, we present our Python package regAL, which allows users to evaluate different active learning strategies for regression problems. With a minimal input of just the dataset in question, but many additional customization and insight options, this package is intended for anyone who aims to perform and understand active learning in their problem-specific scope.
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