regAL: Python Package for Active Learning of Regression Problems
This work provides a tool for researchers in resource-intensive domains to implement active learning for regression, but it is incremental as it packages existing methods rather than introducing new ones.
The authors tackled the scarcity of large datasets in fields like chemistry and materials science by developing regAL, a Python package that enables users to evaluate active learning strategies for regression problems, aiming to reduce the number of required experimental or computational results.
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.