Feature engineering: Difference between revisions

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 {{see also|Machine learning terms}}
 ==Introduction==
-[[Feature engineering]] is a critical process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw [[data]] to improve the accuracy and performance of [[machine learning models]]. Feature engineering is a complex and challenging process that requires domain knowledge, creativity, and expertise in data manipulation techniques. The objective of feature engineering is to transform raw data into a more suitable and informative representation that can be easily understood by machine learning models.
+[[Feature engineering]] is a crucial process in [[machine learning]] that involves selecting, extracting, and transforming relevant [[feature]]s or variables from raw data to enhance the [[accuracy]] and performance of [[machine learning models]]. This complex task necessitates domain knowledge, creativity, and proficiency with data manipulation techniques. The goal of feature engineering is to turn raw [[data]] into an informative representation that can be easily comprehended by machine learning models.
 ==What are features in machine learning?==