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 | 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).
- Context:
- It can support ML Predictive Quality Improvements and Data Preprocessing Processing Improvements.
- 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 more relevant to specific Machine Learning Tasks.
- ...
- Example(s):
- In text mining, a feature generation algorithm might extract n-grams or sentiment scores from raw text.
- In finance, it might generate features like moving averages or volatility measures from stock price data.
- In image recognition, algorithms could generate features by identifying edges, textures, or color histograms in images.
- ...
- Counter-Example(s):
- See: Feature Engineering, Machine Learning Pipeline, Data Preprocessing.