ADVANCING STATISTICAL LEARNING TECHNIQUES FOR HIGH-DIMENSIONAL DATA ANALYSIS AND PREDICTION

Abstract

This study explores advanced statistical learning techniques for analyzing and predicting outcomes in high-dimensional data environments. The research compares the performance of Linear, Ridge, and Lasso regression models to evaluate their effectiveness in enhancing predictive accuracy and model interpretability. Results indicate that Lasso Regression outperforms both Linear and Ridge models by achieving higher explanatory power and lower prediction error, demonstrating its strength in managing sparsity and eliminating redundant predictors. Ridge Regression shows improved stability and control of coefficient variance, confirming its reliability in complex data structures. The study highlights the critical role of regularization in addressing overfitting, improving generalization, and achieving an optimal balance between accuracy and parsimony. Overall, the findings provide valuable insights for data scientists and researchers seeking robust and interpretable solutions for high-dimensional predictive modeling across diverse analytical domains.