regAL: Python Package for Active Learning of Regression Problems

TL;DR

regAL is a Python package that facilitates active learning for regression problems, helping researchers develop accurate models with fewer data points by evaluating various strategies and providing customization options.

Contribution

This work introduces regAL, a Python package that enables evaluation and understanding of active learning strategies specifically for regression tasks, filling a gap in existing tools.

Findings

Supports multiple active learning strategies for regression

User-friendly with minimal input requirements

Provides extensive customization and insight options

Abstract

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.