Enhancing Machine Learning Models through Hyperparameter Optimization with Particle Swarm Optimization
Ariani Indrawati, Intan Nuni Wahyuni · 2023
Hyperparameter tuning plays a crucial role in optimizing the performance of machine learning algorithms. This study explores the effectiveness of Particle Swarm optimization (PSO) in fine-tuning the hyperparameters of three popular machine learning algorithms, including Random Forest, K-Nearest Neighbors, and Support Vector Machine. The studies use two publicly available datasets: the Heart Failure Clinical Records Dataset and the Iris Dataset. For additional comparative analysis, we also compare with the common optimization technique using Random Search. The result reveals that PSO and Random Search have varying impacts on the performance of different algorithms and datasets. The findings provide valuable insights into the behavior of different optimization techniques and their effect on model performance, paving the way for better and more reliable machine learning models in various domains. Through effective hyperparameter optimization, machine learning models can achieve enhanced accuracy.