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. Feature selection techniques are used for several reasons:
- Simplification of models to make them easier to interpret by researchers/users,
- Shorter training times,
- Avoid the curse of dimensionality,
- Enhanced generalization by reducing overfitting (formally, reduction of variance) wiki