Application of Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR)

This example illustrates the application of Partial Least Squares Regression (PLSR) and Principal Component Regression (PCR) for data processing, along with a discussion of their effectiveness. For sample data, both methods have been personally verified to be highly effective. These techniques are particularly useful when dealing with high-dimensional datasets or multicollinearity issues, where PLSR extracts latent variables maximizing covariance between predictors and responses, while PCR performs dimensionality reduction through principal component analysis before regression. Notably, these methods extend beyond data processing to widespread applications in scientific research and engineering, such as pharmaceutical development and materials design. Each method possesses distinct characteristics and advantages: PLSR typically handles response variables more effectively, whereas PCR focuses on predictor variance reduction. The choice between them should be based on specific problem requirements, data structure, and performance validation through metrics like R-squared or cross-validation scores in implementation.