Editing Feature (machine learning) (section)

==Selection and extraction==
{{main|Feature selection|Feature extraction}}

The initial set of raw features can be redundant and large enough that estimation and optimization is made difficult or ineffective. Therefore, a preliminary step in many applications of [[machine learning]] and [[pattern recognition]] consists of [[Feature selection|selecting]] a subset of features, or [[Feature extraction|constructing]] a new and reduced set of features to facilitate learning, and to improve generalization and interpretability.<ref>{{Cite book |last1=Hastie |first1=Trevor |url=https://books.google.com/books?id=eBSgoAEACAAJ |title=The Elements of Statistical Learning: Data Mining, Inference, and Prediction |last2=Tibshirani |first2=Robert |last3=Friedman |first3=Jerome H. |date=2009 |publisher=Springer |isbn=978-0-387-84884-6 |language=en}}</ref>

Extracting or selecting features is a combination of art and science; developing systems to do so is known as [[feature engineering]]. It requires the experimentation of multiple possibilities and the combination of automated techniques with the intuition and knowledge of the [[domain expert]]. Automating this process is [[feature learning]], where a machine not only uses features for learning, but learns the features itself.