Feature Generation Algorithm: Difference between revisions

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(Created page with "A Feature Generation Algorithm is a data processing algorithm that creates new machine learning features from existing data to improve the performance of machine learning models. * <B>Context:</B> ** It can involve techniques such as Feature Extraction, Feature Selection, and Dimensionality Reduction. ** It can be critical in domains where raw data is complex and high-dimensional. ** It can use domain knowledge to create features that are mor...")
 
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A [[Feature Generation Algorithm]] is a [[data processing algorithm]] that creates new [[machine learning feature]]s from existing data to improve the performance of [[machine learning model]]s.
A [[Feature Generation Algorithm]] is a [[data processing algorithm]] that can be implemented by a [[feature generation system]] to solve [[feature generation task]] (sot create new [[ML feature]]s).
* <B>Context:</B>
* <B>Context:</B>
** It can support [[ML Predictive Quality Improvement]]s and [[Data Preprocessing Processing Improvement]]s.
** It can involve techniques such as [[Feature Extraction]], [[Feature Selection]], and [[Dimensionality Reduction]].
** It can involve techniques such as [[Feature Extraction]], [[Feature Selection]], and [[Dimensionality Reduction]].
** It can be critical in domains where raw data is complex and high-dimensional.
** It can be critical in domains where raw data is complex and high-dimensional.

Revision as of 08:20, 15 January 2024

A Feature Generation Algorithm is a data processing algorithm that can be implemented by a feature generation system to solve feature generation task (sot create new ML features).