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| Feature engineering ： ウィキペディア英語版 |
Feature engineering
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.
″When working on a machine learning problem, feature engineering is manually designing what the input x's should be.″—Shayne Miel, "What is the intuitive explanation of feature engineering in machine learning?"
== Features ==
A feature is a piece of information that might be useful for prediction. Any attribute could be a feature, as long as it is useful to the model.
The purpose of a feature, other than being an attribute, would be much easier to understand in the context of a problem. A feature is a characteristic that might help when solving the problem.
抄文引用元・出典: フリー百科事典『 ウィキペディア（Wikipedia）』
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