Perspective Chapter: Enhancing Regression Analysis with Splines and Machine Learning – Evaluation of How to Capture Complex Non-Linear Multidimensional Variables

Abstract

This chapter focuses upon the use of both splines and machine-learning in prediction and the methodology for constructing splines in a predictive context. In the realm of predictive modeling, machine learning and splines represent two pivotal approaches that address the complexity of capturing nonlinear relationships within data. Machine learning excels in identifying intricate patterns and relationships through algorithms that learn from data, making it a powerful tool for prediction across vast datasets. However, its often opaque nature can pose challenges for interpretability. In contrast, splines offer a bridge between the simplicity of linear regression and the complexity of machine learning. By introducing cutpoints in the data, splines allow for flexible modeling of nonlinear trends, providing a clearer interpretation of how independent variables influence the dependent variable across different segments. This makes splines particularly valuable in multivariable regression contexts, where understanding the nuanced effects of covariates is crucial. While machine learning may deliver superior predictive power in some cases, splines provide a compelling balance of predictability and interpretability, especially in scenarios where understanding the underlying model is as important as the accuracy of predictions.