Automated Machine Learning for Classification and Regression: A Tutorial for Psychologists

Machine learning (ML) has extended the scope of psychological research by enabling data-driven discovery of patterns in complex datasets, complementing traditional hypothesis-driven approaches and enriching individual-level prediction. As a principal subfield, supervised ML has advanced mental health diagnostics and behavior prediction through classification and regression tasks. However, the complexity of ML methodologies and the absence of established norms and standardized pipelines often limit its adoption among psychologists. Furthermore, the black-box nature of advanced ML algorithms obscures how decisions are made, making it difficult to identify the most influential variables. Automated ML (AutoML) addresses these challenges by automating key steps such as model selection and hyperparameter optimization, while enhancing interpretability through explainable AI. By streamlining workflows and improving efficiency, AutoML empowers users of all technical levels to implement advanced ML methods effectively. Despite its transformative potential, AutoML remains underutilized in psychological research, with no dedicated educational material available. This tutorial aims to bridge the gap by introducing AutoML to psychologists. We cover advanced AutoML methods, including combined algorithm selection and hyperparameter optimization (CASH), stacked ensemble generalization, and explainable AI. The utility of AutoML is demonstrated using the ‘H2O AutoML’ R package with publicly available psychological datasets, performing regression on multi-individual cross-sectional data and classification on single-individual time-series data. We also provide practical workarounds for ML methods currently unavailable in the package, allowing researchers to use alternative approaches when needed. These examples illustrate how AutoML democratizes ML, making it more accessible while providing advanced methodologies for psychological research.