What is feature selection?

Feature selection, also known as variable selection, attribute selection or variable subset selection, is the process of selecting a subset of relevant features (variables, predictors) for use in model construction. The central assumption when using a feature selection technique is that the data contains many redundant or irrelevant features. Redundant features are those which provide no more information than the currently selected features, and irrelevant features provide no useful information in any context. Feature selection techniques are used for several reasons: to simplify models to make them easier to interpret by researchers/users, to shorten training times, to avoid the curse of dimensionality, to reduce variance and improve generalization, and to improve data quality. Common methods include filtering, wrapper methods, and embedded methods.