Machine Learning Validation via Rational DatasetSampling with astartes
Jackson Burns, Kevin Spiekermann, Himaghna Bhattacharjee, Dionisios G. Vlachos, William H. Green
Abstract
Machine Learning (ML) has become an increasingly popular tool to accelerate traditional workflows. Critical to the use of ML is the process of splitting datasets into training, validation, and testing subsets that are used to develop and evaluate models. Common practice in the literature is to assign these subsets randomly. Although this approach is fast and efficient, it only measures a model’s capacity to interpolate. Testing errors from random splits may be overly optimistic if given new data that is dissimilar to the scope of the training set; thus, there is a growing need to easily measure performance for extrapolation tasks. To address this issue, we report astartes, an open-source Python package that implements many similarityand distance-based algorithms to partition data into more challenging splits. Separate from astartes, users can then use these splits to better assess out-of-sample performance with any ML model of choice. This publication focuses on use-cases within cheminformatics. However, astartes operates on arbitrary vector inputs, so its principals and workflow are generalizable to other ML domains as well. astartes is available via the Python package managers pip and conda and is publicly hosted on GitHub (github.com/JacksonBurns/astartes).