Estimasi Parameter Least Absolute Shrinkage and Selection Operator (Lasso) Menggunakan Parameter Cyclic Coordinate Descent

Abstract

Least Absolute Shrinkage and Selection Operator (Lasso) regression is one method that can overcome multicollinearity problems by shrinking some regression coefficients to exactly zero. Cyclic Coordinate Descent algorithm is efficiently and structurally used to determine parameter estimates especially for datasets with many independent variables. The objective of this study is to obtain accurate and interpretable parameter estimates, especially in the face of multicollinearity problems. To evaluate the performance of this method, three generated datasets with different levels of multicollinearity: high, medium, and low were used and obtained from the python programming language. The results of this study show that the Lasso method with the CCD algorithm is suitable for data containing high multicollinearity, where there is a shrinkage of variables to zero in highly correlated independent variables. Based on the research results, it can be concluded that the Lasso method with the CCD algorithm produces a simpler model but still has high prediction accuracy on data with high multicollinearity. Meanwhile, for data with moderate and low multicollinearity, the regression model generated by the OLS method is superior because all variables still contribute significantly, so the Lasso penalty can actually reduce the model performance.