Multicollinearity Issue

Multicollinearity, the presence of high correlation between predictor variables in a dataset, poses a significant challenge in statistical modeling and machine learning. Current research focuses on mitigating its effects through various strategies, including penalized regression models, dimensionality reduction techniques like clustering (e.g., DBSCAN), and the use of algorithms less sensitive to multicollinearity such as tree-based methods (e.g., Random Forests). Addressing multicollinearity is crucial for building reliable and interpretable models, particularly in applications like environmental modeling (e.g., carbon emission prediction) and data imputation where accurate and explainable results are essential. Improved handling of multicollinearity enhances the trustworthiness and generalizability of models across diverse scientific domains.