Feature selection is a dimensionality reduction technique that selects only a subset of measured features (predictor variables) that provide the best predictive power in modeling the data. It is particularly useful when dealing with very high-dimensional data or when modeling with all features is undesirable.
Feature selection can be used to:
There are several common approaches to feature selection:
Another dimensionality reduction approach is to use feature extraction or feature transformation techniques, which transform existing features into new features (predictor variables) with the less descriptive features dropped.
Approaches to feature transformation include:
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