Paper Contents
Abstract
Machine learning classification algorithms play a central role in a wide range of applications, from medical diagnosis to financial forecasting and image recognition. This review explores key machine learning classification algorithms, including traditional methods (decision trees, SVM), ensemble techniques (random forests, boosting), and deep learning models (neural networks, CNNs). By analyzing three influential papers, we compare their theoretical foundations, performance, and application to real-world problems. The review highlights strengths, challenges, and emerging trends, such as transfer learning and model interpretability. The findings offer valuable insights for selecting optimal algorithms and navigating challenges like data imbalance and computational efficiency in classification tasks.
Copyright
Copyright © 2024 Tejo Madhuri Dwarampudi. This is an open access article distributed under the Creative Commons Attribution License.
