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

It is common to build predictive models for complex phenotypes, such as diseases, where sample sizes are often limited by the cost of data collection. Machine learning and statistical methods are widely used in these cases. However, they may fail to identify high-quality features and lead to suboptimal models with high underfitting or overfitting risks. Here, we present mBoost to identify low-quality features and suboptimal models to enhance phenotype prediction. Importantly, it provides confidence on model performance, helping researchers avoid the frustrating cycle of model switching for possibly better performance. We demonstrate mBoost with simulations and real-data examples. Specifically, mBoost identifies optimal depth and width for a deep neural network in a drug example, detects suboptimal models in microbiome cohorts, and proposes a measure for telling whether the trained model is applicable to new data. Thus, mBoost will serve as a practical tool for model building and diagnosis.