SVM Prediction Interface for MATLAB Implementation

This documentation shares insights about implementing prediction interfaces using Support Vector Machines (SVM) in MATLAB. SVM represents a robust machine learning algorithm capable of solving diverse prediction problems through supervised learning approaches. The MATLAB environment provides an integrated interface that streamlines SVM implementation with key functionalities including: data import through functions like readtable() or xlsread(), model training using fitcsvm() for classification or fitrsvm() for regression, and parameter optimization via automatic hyperparameter tuning. The interface supports kernel selection (linear, polynomial, RBF) and incorporates cross-validation techniques to prevent overfitting. Through this comprehensive toolkit, users can effectively leverage SVM algorithms to enhance prediction accuracy and analytical outcomes while gaining deeper understanding of feature engineering and decision boundary optimization in machine learning workflows.