题目：Identifying low-quality features and suboptimal models to enhance phenotype prediction

摘要：Biomedical researchers increasingly rely on machine learning to predict disease outcomes and treatment responses from omics and clinical data. Yet, these models are often developed using small or varied patient groups, and this makes it difficult to know whether a model has learned meaningful biological patterns or has simply fit the available data. Standard measures of predicting accuracy do not always reveal whether a model is too simple, unnecessarily complex, built with uninformative features, or likely to perform well with different datasets. mBoost addresses these challenges by introducing a framework for examining these issues. Based on this assessment, mBoost can help researchers decide when to adjust the model, refine features, or train a new model for a different dataset.