Feature selection is the process of choosing a relevant subset of available features for use in model construction. It can improve model performance, reduce complexity, decrease overfitting, and make results easier to interpret.
In machine learning and statistics, 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.