A General Approach for Determining Applicability Domain of Machine Learning Models

Understand

Knowledge of the domain of applicability of a machine learning model is essential to ensuring accurate and reliable model predictions.

• In this work, we develop a new and general approach of assessing model domain and demonstrate that our approach provides accurate and meaningful domain designation across multiple model types and material property data sets.
• Our approach assesses the distance between data in feature space using kernel density estimation, where this distance provides an effective tool for domain determination.
• We show that chemical groups considered unrelated based on chemical knowledge exhibit significant dissimilarities by our measure.