Feature selection

In machine learning, feature selection is the process of selecting a subset of relevant <a href="/facts/Feature_(machine_learning)/nzAhYxfu">features</a> (variables, predictors) for use in model construction. Feature selection techniques are used for several reasons:

<ul><li>simplification of models to make them easier to interpret,</li>
<li>shorter training times,</li>
<li>to avoid the <a href="/facts/Curse_of_dimensionality/dk7SnY6L">curse of dimensionality</a>,</li>
<li>improve the compatibility of the data with a certain learning model class,</li>
<li>to encode inherent <a href="/facts/Symmetric_space/AWYuHfNp">symmetries</a> present in the input space.</li></ul>
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. 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.
<a href="/facts/Feature_extraction/BSMX5e8I">Feature extraction</a> 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).

Feature selection open-in-new

Feature selection