Comparative Analysis of Traditional Machine Learning and Deep Learning Methods: Performance on Datasets of Varying Complexity

Abstract

This study presents a comparative analysis of traditional machine learning and deep learning methods, evaluating their performance on datasets of varying complexity. Traditional methods such as Random Forests and Naive Bayes exhibit high accuracy and computational efficiency for low-complexity tasks, making them suitable for real-time applications. In contrast, deep learning techniques, including Convolutional Neural Networks (CNNs) and Autoencoders, excel in high-complexity tasks such as image and speech processing but require significant computational resources and longer training times. By analyzing their respective strengths and limitations, this research provides insights into selecting the appropriate algorithm based on dataset complexity and task requirements. The findings highlight opportunities for hybrid models to combine the benefits of both approaches, addressing computational efficiency and accuracy. Future research directions include enhancing deep learning model interpretability and optimizing preprocessing techniques to improve performance in data-scarce scenarios.