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- Q22245680 subject Q7015116.
- Q22245680 abstract "Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive. The need for manual feature engineering can be obviated by automated feature learning.Feature engineering is an informal topic, but it is considered essential in applied machine learning.Coming up with features is difficult, time-consuming, requires expert knowledge. "Applied machine learning" is basically feature engineering.When working on a machine learning problem, feature engineering is manually designing what the input x's should be.".
- Q22245680 wikiPageExternalLink DeepLearning-Mar2013.pptx.
- Q22245680 wikiPageWikiLink Q10996045.
- Q22245680 wikiPageWikiLink Q17013334.
- Q22245680 wikiPageWikiLink Q191183.
- Q22245680 wikiPageWikiLink Q1921834.
- Q22245680 wikiPageWikiLink Q2061913.
- Q22245680 wikiPageWikiLink Q2539.
- Q22245680 wikiPageWikiLink Q2846695.
- Q22245680 wikiPageWikiLink Q331309.
- Q22245680 wikiPageWikiLink Q446488.
- Q22245680 wikiPageWikiLink Q5178903.
- Q22245680 wikiPageWikiLink Q5439682.
- Q22245680 wikiPageWikiLink Q620622.
- Q22245680 wikiPageWikiLink Q7015116.
- Q22245680 wikiPageWikiLink Q7239673.
- Q22245680 comment "Feature engineering is the process of using domain knowledge of the data to create features that make machine learning algorithms work. Feature engineering is fundamental to the application of machine learning, and is both difficult and expensive.".
- Q22245680 label "Feature engineering".