Machine learning is widely used for solving problems in different areas by learning patterns from data. However, real-world datasets may contain a large number of features, including irrelevant, redundant, or less informative features. Using all available features can increase computational requirements, make models more complex, and sometimes negatively affect prediction performance. Feature selection is an important data preprocessing technique that focuses on selecting the most relevant features from a dataset while removing unnecessary features.This research paper focuses on understanding and analyzing different feature selection techniques used in machine learning. It discusses the major categories of feature selection methods, including filter methods, wrapper methods, and embedded methods. Techniques such as correlation-based selection, chi-square, mutual information, Recursive Feature Elimination (RFE), Lasso, and tree-based feature importance are considered. The study also examines the advantages, limitations, computational requirements, and influence of different feature selection approaches on machine learning models.The main purpose of this study is to understand how selecting relevant features can reduce data complexity, improve model interpretability, reduce computational requirements, and potentially improve machine learning performance. The study uses descriptive and comparative analysis based on existing research and experimental results obtained from selected datasets and machine learning models.

 

Keywords: Feature Selection, Machine Learning, Feature Engineering, Filter Methods, Wrapper Methods, Embedded Methods, Dimensionality Reduction, Classification.