Visualising machine learning model outputs in data analytics: a systematic review

As data analytics increasingly rely on machine learning models for forecasting, classification, and prediction, effective visualisation becomes essential for transforming model outputs into practical insight. Yet the ways these outputs are visualised, and the evidence supporting those designs, remain fragmented across domains. This paper presents a systematic literature review of visualising machine learning model outputs in data analytics, focusing on how predicted outputs are communicated to end-users alongside performance and uncertainty information, and how these visual systems are evaluated in practice. Following PRISMA, we screened 330 articles from ACM Digital Library, IEEE Xplore, and PubMed and included 88 peer-reviewed studies published between Jan 2015 and July 2024. Across the corpus, we identify (1) recurring visual encoding and interaction patterns for interpreting predictions in temporal, spatio-temporal, and event-based settings; (2) common strategies for presenting model validation, calibration, and uncertainty; and (3) a wide range of evaluation approaches, from informal expert feedback to controlled user studies and deployments. The synthesis highlights persistent gaps in rigorous and comparable evaluation, challenges in supporting diverse user goals and expertise levels, and practical constraints that arise in operational contexts. We conclude by distilling practical implications for designing and assessing predictive visualisations, as well as outlining recommendations for future research and practice, with particular attention to improving uncertainty communication, strengthening evaluation rigour and comparability, and adopting evaluation methods that better reflect operational data analytics practice.