Feature Selection

Feature selection is a process in machine learning that involves selecting a subset of relevant features for use in model construction. It aims to improve model performance by eliminating irrelevant or redundant data, which can lead to overfitting and increased computational costs. Common techniques for feature selection include filter methods, wrapper methods, and embedded methods, each with its own advantages and use cases. This process is crucial in high-dimensional datasets where many features may not contribute positively to the predictive power of the model.