Feature engineering: Difference between revisions

===Feature selection===

[[Feature selection]] involves selecting a subset of relevant features from a larger set of features. This can be done using various techniques such as [[correlation analysis]], [[mutual information]], [[chi-square tests]], and [[recursive feature elimination]]. The goal of feature selection is to reduce the dimensionality of the data while maintaining or improving the performance of the machine learning model.

===Feature extraction===

[[Feature extraction]] involves creating new features from existing features by applying various mathematical or statistical transformations. Examples of feature extraction techniques include [[Principal Component Analysis]] (PCA), [[Singular Value Decomposition]] (SVD), and [[Non-negative Matrix Factorization]] (NMF). The goal of feature extraction is to create a more informative and compact representation of the data that can improve the performance of machine learning models.

===Feature transformation===

[[Feature transformation]] involves transforming the original features by applying mathematical or statistical functions such as logarithmic, exponential, or power functions. The goal of feature transformation is to [[normalize]] the data or make it more suitable for a particular machine learning model. Examples of feature transformation techniques include [[scaling]], [[centering]], and [[normalization]].