Abstract
Linear regression is the most representative “machine learning” method to build models for value prediction and classification from training data. It offers a study in contrasts:
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Linear regression has a beautiful theoretical foundation yet, in practice, this algebraic formulation is generally discarded in favor of faster, more heuristic optimization.
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Linear regression models are, by definition, linear. This provides an opportunity to witness the limitations of such models, as well as develop clever techniques to generalize to other forms.
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Linear regression simultaneously encourages model building with hundreds of variables, and regularization techniques to ensure that most of them will get ignored.
An unsophisticated forecaster uses statistics as a drunken man uses lamp posts – for support rather than illumination.
– Andrew Lang
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Skiena, S.S. (2017). Linear and Logistic Regression. In: The Data Science Design Manual. Texts in Computer Science. Springer, Cham. https://doi.org/10.1007/978-3-319-55444-0_9
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DOI: https://doi.org/10.1007/978-3-319-55444-0_9
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