Feature engineering: Difference between revisions

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 Feature engineering can be broadly classified into three main types: [[feature selection]], [[feature extraction]], and [[feature transformation]].
-===Feature selection===
+===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 selection]] is the process of selecting a subset of relevant features from an expansive set. This can be done through various techniques like [[correlation analysis]], [[mutual information]], [[chi-square tests]] and [[recursive feature elimination]]. The aim is to reduce data dimensionality while maintaining or improving the performance of a machine learning model.
-===Feature extraction===
+===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 extraction]] is the process of creating new features from existing data through 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 data which could then enhance machine learning models' performance.
-===Feature transformation===
+===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]].
+[[Feature transformation]] involves altering the original features by applying mathematical or statistical functions such as logarithmic, exponential or power functions. The purpose of feature transformation is to [[normalize]] data or make it more suitable for a machine learning model. Common feature transformation techniques include [[scaling]], [[centering]] and [[normalization]].
 ==How is feature engineering done in practice?==