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World Journal of Engineering
Research and Technology

( An ISO 9001:2015 Certified International Journal )

An International Peer Reviewed Journal for Engineering Research and Technology

ISSN 2454-695X

Impact Factor : 5.924

ICV : 79.45

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Abstract

COMPARATIVE STUDY OF MULTICOLLINEARITY USING REGULARIZATION METHOD

Ngozi Nzelu*

ABSTRACT

In this study, Ridge, Lasso and Elastic Net Regression were compared as a regularization method to determine the model that will be better to handle multicollinearity in a dataset, especially in the area of health. Variance inflation factors were used to dictate multicollinearity in liver patient record data set and and which correspond to Total Protiens (TP) and Albumin Ratio Albumin (ALB) respectively were highly correlated. The Statistical analysis result shows that the Elastic Net Regression performed better than Ridge and lasso Regression with minimum RMSE of 0.4311013 and highest R-square of 15.62376.

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