A Study on Hyperparameter Tuning in Support Vector Machines and its Impact on Model Accuracy

This study investigates the impact of hyperparameter tuning on the accuracy of Support Vector Machines (SVMs), focusing on the comparison between three widely used tuning techniques: Grid Search, Random Search, and Bayesian Optimization. SVMs are highly sensitive to hyperparameters such as the regularization parameter (C) and gamma, which significantly influence the model's ability to generalize from training data. Through a series of experiments, we evaluate the effectiveness of each tuning method in optimizing these parameters to enhance model performance. Our results show that Bayesian Optimization consistently outperforms both Grid Search and Random Search in terms of accuracy, while also being more computationally efficient. These findings highlight the importance of selecting appropriate tuning strategies in machine learning workflows, especially in applications where model accuracy and computational efficiency are critical.