Least Squares Support Vector Machine Regression for Multidimensional Pyrim Data
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In this regression task using multidimensional pyrim data, we employ the Least Squares Support Vector Machine (LS-SVM) machine learning algorithm. To facilitate implementation, the LS-SVM toolbox must be downloaded - this MATLAB/Python-based toolkit provides essential functions for data preprocessing, model selection, and performance evaluation. The toolbox typically includes core functions like trainlssvm for model training and simlssvm for prediction, which solve the system of linear equations using conjugate gradient methods instead of quadratic programming. Additionally, practical implementation requires careful attention to data quality and quantity, along with critical parameter selection such as regularization parameter gamma and kernel function parameters (RBF kernel sigma). These factors significantly impact model performance and require systematic validation through techniques like cross-validation to ensure accurate and reliable results.
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