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==Why is feature engineering important?==
==Why is feature engineering important?==
Feature engineering is important in machine learning for several reasons. First, it helps to improve the performance and [[accuracy]] of machine learning models by providing a more informative and discriminative representation of the data. Second, it helps to reduce the [[dimensionality]] of the data by removing irrelevant or redundant features, which can simplify the learning process and improve computational efficiency. Third, it can help to address issues such as [[overfitting]] and [[underfitting]] by providing a better balance between [[bias]] and [[variance]]. Finally, feature engineering can help to enhance the interpretability and explainability of machine learning models, which is essential in many real-world applications.
Feature engineering is essential in machine learning for several reasons. Firstly, it improves performance and [[accuracy]] of [[models]] by providing a more informative representation of data. Secondly, it reduces [[dimensionality]] by eliminating irrelevant or redundant features which simplifies the learning process and increases computational efficiency. Thirdly, feature engineering helps address issues like [[overfitting]] or [[underfitting]] by maintaining an appropriate balance between [[bias]] and [[variance]]. Finally, feature engineering improves the interpretability and explainability of machine learning models - essential qualities required in many real-world applications.


==What are the types of feature engineering?==
==What are the types of feature engineering?==
Feature engineering can be broadly classified into three main types: [[feature selection]], [[feature extraction]], and [[feature transformation]].
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?==
==How is feature engineering done in practice?==
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By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes.
By choosing the right features, we can help the computer learn more quickly and accurately. It's like having the right tools to put a puzzle together faster and with fewer mistakes.


[[Category:Terms]] [[Category:Machine learning terms]]
[[Category:Terms]] [[Category:Machine learning terms]] [[Category:not updated]]
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