Machine learning models and over-fitting considerations

Abstract

Machine learning models may outperform traditional statistical regression algorithms for predicting clinical outcomes. Proper validation of building such models and tuning their underlying algorithms is necessary to avoid over-fitting and poor generalizability, which smaller datasets can be more prone to. In an effort to educate readers interested in artificial intelligence and model-building based on machine-learning algorithms, we outline important details on cross-validation techniques that can enhance the performance and generalizability of such models.

Keywords: Machine learning; Over-fitting; Cross-validation; Hyper-parameter tuning

Core Tip: Machine learning models are increasingly being used in clinical medicine to predict outcomes. Proper validation techniques of these models are essential to avoid over-fitting and poor generalization on new data.