Implementing Partial Least Squares Regression Model Using MATLAB Software

This article provides a comprehensive guide on implementing Partial Least Squares (PLS) Regression models using MATLAB software, along with practical applications of this modeling technique. We begin by exploring the fundamental concepts and theoretical background of PLS regression. Subsequently, we demonstrate how to build PLS regression models in MATLAB using built-in functions like plsregress(), which handles both predictor and response variable matrices while calculating component weights and loadings. The implementation section covers key steps including data preprocessing, dimension reduction through latent variables, and model validation techniques. We discuss important parameters such as the number of components selection using cross-validation methods like crossval() and perfcurve() functions for performance evaluation. The article also explains how to interpret model results through variance explained metrics and regression coefficients analysis. Furthermore, we examine result interpretation and evaluation methodologies, including how to assess model performance using metrics like RMSE and R-squared values through predictive residual analysis. The discussion extends to practical applications across various domains such as chemometrics, bioinformatics, and financial modeling, while addressing the model's limitations in handling nonlinear relationships and high-dimensional data scenarios. Throughout this guide, readers will learn to implement PLS regression models in MATLAB using appropriate statistical functions, understand component extraction algorithms, and gain insights into the model's practical value across different research fields. The implementation includes code examples for data standardization, model fitting, and result visualization using MATLAB's plotting capabilities.