SVM_Toolbox: Support Vector Machine Implementation and Analysis Toolkit

In machine learning applications, the Support Vector Machine (SVM) algorithm serves as a robust solution for both classification and regression tasks. The SVM Toolbox provides comprehensive implementation capabilities through key functions like svmtrain() for model fitting and svmpredict() for inference. The toolbox supports multiple kernel functions including linear, polynomial, and radial basis function (RBF) kernels, which can be specified via kernel_type parameters. Data preprocessing features include automatic scaling through svm_scale() function and cross-validation support for parameter optimization using grid search techniques. The toolbox incorporates visualization modules for decision boundaries through plot_svm_boundary() and provides model interpretation tools featuring support vector extraction and margin calculation algorithms. For hyperparameter tuning, users can implement custom optimization loops with C (regularization) and gamma (kernel coefficient) parameters, while the built-in model evaluation metrics include accuracy_score() for classification and mean_squared_error() for regression tasks. The toolbox's modular design allows seamless integration with Python's scikit-learn or MATLAB's Statistics and Machine Learning Toolbox workflows, making it ideal for both research prototyping and production deployments.