Mitigating the Multicollinearity Problem and Its Machine Learning Approach: A Review

Technologies have driven big data collection across many fields, such as genomics and business intelligence. This results in a significant increase variables points (observations) collected stored. Although this presents opportunities to better model the relationship between predictors response variables, also causes serious problems during analysis, one of which is multicollinearity problem. The two main approaches used mitigate are variable selection methods modified estimator methods. However, may negate efforts collect more new eventually be dropped from modeling, while recent studies suggest that optimization via machine learning handle with than statistical estimators. Therefore, study details chronological developments effects up-to-date recommendations multicollinearity.