Classifier with Graphical User Interface

This documentation discusses the MATLAB software package and its core functionalities. The package provides comprehensive classifier implementations with graphical user interface (GUI) components, primarily designed for binary classification tasks. The system architecture employs machine learning algorithms such as Support Vector Machines (SVM) or Decision Trees, which can be configured through intuitive GUI elements like dropdown menus and parameter sliders. While optimized for two-class separation, the underlying codebase utilizes scalable design patterns that allow extension to multi-class classification through methods like one-vs-all or one-vs-one strategies. This functionality proves particularly valuable for data analysis and pattern recognition, as it enables users to process complex datasets through interactive visualization tools and real-time result displays. The implementation includes key MATLAB functions such as fitcsvm for model training and predict for classification inference, all accessible through programmatic interfaces or GUI controls. Overall, this MATLAB package represents a versatile and robust tool applicable across various classification scenarios, combining algorithmic power with user-friendly interaction design.