Feature selection

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In machine learning, feature 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,^[1]
• shorter training times,^[2]
• to avoid the curse of dimensionality,^[3]
• improve the compatibility of the data with a certain learning model class,^[4]
• to encode inherent symmetries present in the input space.^[5][6][7][8]

The central premise when using feature selection is that data sometimes contains features that are redundant or irrelevant, and can thus be removed without incurring much loss of information.^[9] Redundancy and irrelevance are two distinct notions, since one relevant feature may be redundant in the presence of another relevant feature with which it is strongly correlated.^[10]

Feature extraction creates new features from functions of the original features, whereas feature selection finds a subset of the features. Feature selection techniques are often used in domains where there are many features and comparatively few samples (data points).