MACHINE LEARNING: TECHNIQUES, APPLICATIONS, AND METRICS FOR ENHANCED VEHICLE PERFORMANCE
Sažetak
Machine learning allows systems to autonomously learn from data and improve performance without direct programming, providing robust tools for identifying patterns, forecasting outcomes, and optimising complex processes. This paper offers a comprehensive overview of machine learning, beginning with an examination of its core categories: supervised learning, unsupervised learning, semi-supervised learning, reinforcement learning, and deep learning. The review considers essential algorithms associated with each machine learning category, providing insights into their functions and real-world applications. The paper also discusses widely used evaluation metrics for regression models, including Mean Absolute Error (MAE), Mean Squared Error (MSE), Root Mean Squared Error (RMSE), and R-squared (R²), which are essential for assessing predictive performance and guiding model selection. The article examines numerous real-world applications of machine learning across various sectors, including healthcare, banking, transportation, marketing, and cybersecurity, illustrating how these technologies are transforming modern processes and delivering tangible benefits.
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