A Comparative Study of Penalized Regression and Machine Learning Algorithms in High Dimensional Scenarios

. With the prevalence of big data in recent years, the importance of modeling high dimensional data and selecting important features has increased greatly. High dimensional data is common in many fields such as genome decoding, rare disease identification, and environmental modeling. However, most traditional regression machine learning models are not designed to handle high dimensional data or conduct variable selection. In this paper, we investigate the use of penalized regression meth-ods such as ridge, least absolute shrinkage and selection operation, elastic net, smoothly clipped absolute deviation, and minimax concave penalty compared to traditional machine learning models such as random forest, XGBoost, and support vector machines. We compare these models using factorial design methods for Monte Carlo simulations in 540 environments, with factors being the response variable, number of predictors, number of samples, signal to noise ratio, covariance matrix, and correlation strength. We also compare different models using empirical data to evaluate their viability in real-world scenarios. We evaluate the models using the training and test mean squared error, variable selection accuracy, β -sensitivity, and β -specificity. We found that the performance of penalized regression models is comparable with traditional machine learning algorithms in most high-dimensional situations. The analysis helps to create a greater understanding of the strengths and weaknesses of each model type and provide a reference for other researchers on which machine learning techniques they should use, depending on a range of factors and data environments. Our study shows that penalized regression techniques should be included in predictive modelers’ toolbox.