ABSTRACT
Machine learning (ML) has demonstrated a powerful ability in learning complex patterns or inherent dynamics from observed data. Most machine learning models are black-box, in that the internal behaviour of the models is opaque and thus unknown to no one. However, in many real applications, e.g., in many medical and healthcare domains, it is significantly useful or necessary to explicitly know the internal compositions, combinations or interactions of the models to be used for one purpose or another. Therefore, the interest in interpreting machine learning models has increasingly grown in recent years, especially for cases where users need to do predictions using the models and require explanations for an insightful understanding of drivers that cause the predicted behaviour. This study introduces a novel interpretable machine learning method based on contrastive learning and Non-linear Auto Regressive Moving Average with exogenous inputs (NARMAX) model (referred to as CL-NARMAX thereafter). The proposed method provides a glass-box model, where the input-output relationship and interactions between the input variables can be written down, so as the model cannot only be applied for predicting future behaviour but also for explaining the relevant "reasons" behind the predicted behaviour.
