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The aim is to review methods for addressing multicollinearity issues in data analysis, focusing on machine learning techniques.

April 12, 2022MathematicsOpen Access

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

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Review discusses methods to mitigate multicollinearity in data analysis, emphasizing machine learning benefits.

The aim is to review methods for addressing multicollinearity issues in data analysis, focusing on machine learning techniques.
Discussed the two primary approaches to mitigate multicollinearity: variable selection methods and modified estimator methods.
Outlined the limitations of variable selection regarding data loss.
Described how machine learning optimization approaches outperform traditional statistical estimators for managing multicollinearity.
Variable selection methods can hinder data usage by discarding valuable variables.
Machine learning approaches show superior performance in handling multicollinearity compared to conventional estimators.

Chan et al. (2022) studied this question.

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