Extended Least Absolute Shrinkage with Selection Operator Technique for Sparse Regression Modeling with High Dimensional Date

This study was carried out on extended least absolute shrinkage with selection operator technique for sparse regression modeling with high dimensional date. The objective of this paper is to advance sparse regression modeling techniques for high-dimensional data through the enhancement of the LASSO algorithm and its application, by developing an extended LASSO model to improve variable selection in high-dimensional datasets. The study posits that the extended LASSO algorithm will effectively address key challenges in high-dimensional data analysis, including multicollinearity and over-fitting. The research design focuses on LASSO formulation and sparsity-Inducing properties using least absolute shrinkage and selection operator (LASSO) formulation. Regularization techniques and their impact on bias-variance trade-off. Regularization techniques adjust the model's complexity to achieve an optimal balance between bias and variance, thereby improving the model's performance on unseen data. This paper hypothesized that the extended LASSO algorithm can be successfully applied to real-life high-dimensional datasets, resulting in improved model performance and greater applicability in various fields. Conclusively, this study offers a valuable contribution to both the theoretical framework of sparse regression modeling and its practical use in tackling high-dimensional data challenges, leading to better decision-making across a range of industries.