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Feature engineering

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Feature engineering is a preprocessing step in supervised machine learning and statistical modeling[1] which transforms raw data into a more effective set of inputs. Each input comprises several attributes, known as features. By providing models with relevant information, feature engineering significantly enhances their predictive accuracy and decision-making capability.[2][3][4]

Beyond machine learning, the principles of feature engineering are applied in various scientific fields, including physics. For example, physicists construct dimensionless numbers such as the Reynolds number in fluid dynamics, the Nusselt number in heat transfer, and the Archimedes number in sedimentation. They also develop first approximations of solutions, such as analytical solutions for the strength of materials in mechanics.[5]

  1. ^ Hastie, Trevor; Tibshirani, Robert; Friedman, Jerome H. (2009). The Elements of Statistical Learning: Data Mining, Inference, and Prediction. Springer. ISBN 978-0-387-84884-6.
  2. ^ Sharma, Shubham; Nayak, Richi; Bhaskar, Ashish (2024-05-01). "Multi-view feature engineering for day-to-day joint clustering of multiple traffic datasets". Transportation Research Part C: Emerging Technologies. 162: 104607. Bibcode:2024TRPC..16204607S. doi:10.1016/j.trc.2024.104607. ISSN 0968-090X.
  3. ^ Shalev-Shwartz, Shai; Ben-David, Shai (2014). Understanding Machine Learning: From Theory to Algorithms. Cambridge: Cambridge University Press. ISBN 9781107057135.
  4. ^ Murphy, Kevin P. (2022). Probabilistic Machine Learning. Cambridge, Massachusetts: The MIT Press (Copyright 2022 Massachusetts Institute of Technology, this work is subject to a Creative Commons CC-BY-NC-ND license). ISBN 9780262046824.
  5. ^ MacQueron C (2021). SOLID-LIQUID MIXING IN STIRRED TANKS : Modeling, Validation, Design Optimization and Suspension Quality Prediction (Report). doi:10.13140/RG.2.2.11074.84164/1.

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