A Classic SVM Visualization Tool with Dual Algorithm Support

This is an exceptionally classic SVM visualization tool that performs outstandingly well. It implements two major SVM variants popular in contemporary machine learning practice - typically including C-SVC (Support Vector Classification) and nu-SVC implementations. The tool significantly aids users in understanding and analyzing SVM algorithms through interactive visualization of decision boundaries, support vectors, and margin calculations. It provides an intuitive graphical interface with comprehensive functionality, enabling users to easily explore and experiment with different SVM models by adjusting kernel parameters (linear, polynomial, RBF) and regularization constants. The underlying code demonstrates core SVM concepts through mathematical formulation visualization and includes practical implementations of key functions like kernel matrix computation and quadratic programming optimization. Both beginners and professionals can leverage this tool to gain deeper insights into SVM mechanics, enhancing their learning and research experience through hands-on parameter tuning and real-time result visualization.