FEATURE ENGINEERING IN MACHINE LEARNING: SELECTION, EXTRACTION, AND THEIR IMPACT ON MODEL PERFORMANCE

Feature selection and extraction are fundamental steps in the data pre-processing phase of Machine Learning (ML).These steps significantly impact the models' performance, interpretability, and computational efficiency.Feature selection involves identifying the most relevant attributes from a dataset while preserving critical information.On the other hand, feature extraction transforms raw data into meaningful representations to enable more effective learning.The current study provides a comprehensive overview of feature selection and extraction, highlighting their importance, types of methods, and applications across various domains.The study also presents the challenges such as the Curse of Dimensionality, the presence of noisy or irrelevant features, and the trade-off between performance and interpretability limit their effectiveness.The study concludes with insights into emerging trends and future directions, including hybrid approaches and the integration of feature selection with deep learning to enhance scalability and cross-domain adaptability.