% Create dataset objects X_obj = dataset(X, 'name', 'NIR Spectra', 'axislabels', 'Samples', 'Wavelengths'); Y_obj = dataset(Y, 'name', 'Octane', 'axislabels', 'Samples', 'Components');
Partial Least Squares (PLS) is a multivariate statistical technique used for modeling the relationship between a set of independent variables (X) and a set of dependent variables (Y). PLS is particularly useful when dealing with high-dimensional data, multicollinearity, and non-normality. matlab pls toolbox
The toolbox provides a robust environment for building predictive and descriptive models. Key algorithms and features include: % Create dataset objects X_obj = dataset(X, 'name',
The toolbox includes a that converts your PLS model into standalone MATLAB code, C-code, or even a spreadsheet. This allows you to embed predictive models into online process control systems. Key algorithms and features include: The toolbox includes
: Used to build predictive models where the number of variables exceeds the number of samples, common in spectroscopy. Classification
and Cluster Analysis to identify patterns and outliers in unsupervised datasets. Advanced Regression & Classification
: Standard methods like Partial Least Squares (PLS), Principal Components Analysis (PCA), and Nonlinear methods like locally weighted regression.