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Machine learning and. 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 three reasons:
Feature templates: When designing a feature set, you usually quickly turn from coding individual features (such as âthis word is predeced by a preposition and a determinerâ) to implementing feature templates (such as âthe two preceding POSs are X a Yâ) Feature combination: linear models cannot handle some dependencies between features (e.g. XOR with binary operations, polynomial dependencies with real-valued features) - feature combinations might work better. Both lead to quick growth of the number of features.