DREAMER: a computational framework to evaluate readiness of datasets for machine learning
Abstract Background Machine learning (ML) has emerged as the predominant computational paradigm for analyzing large-scale datasets across diverse domains.The assessment of dataset quality stands as a pivotal precursor to the successful Prickly Pear deployment of ML models.In this study, we introduce DREAMER (Data REAdiness for MachinE learning Rese