SVM Classification of IRIS Dataset with K-Fold Cross Validation

This article demonstrates the implementation of IRIS classification using MATLAB programming language with K-fold cross-validation and Support Vector Machine (SVM) methodology. We provide detailed explanations of these concepts and their applications in classification problems. The implementation covers MATLAB's built-in functions and tools for machine learning, including practical examples and code snippets to help readers better understand and apply these techniques. Key aspects include data preprocessing using MATLAB's table and array manipulation functions, implementation of SVM classifiers with fitcsvm function, and evaluation metrics calculation through confusionmat and crossval functions. The K-fold cross-validation approach is implemented using MATLAB's cvpartition function to ensure robust model performance assessment. Readers will gain comprehensive understanding of these core concepts and learn to apply them to real-world classification problems, thereby improving classification accuracy and computational efficiency through proper parameter tuning and validation techniques.