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Published in:
2018 | OriginalPaper | Chapter
18. Regularized Linear Modeling and Controlled Variable Selection
Author : Ivo D. Dinov
Published in: Data Science and Predictive Analytics
Publisher: Springer International Publishing
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Abstract
Many biomedical and biosocial studies involve large amounts of complex data, including cases where the number of features (k) is large and may exceed the number of cases (n). In such situations, parameter estimates are difficult to compute or may be unreliable as the system is underdetermined. Regularization provides one approach to improve model reliability, prediction accuracy, and result interpretability. It is based on augmenting the primary fidelity term of the objective function used in the model-fitting process with a dual regularization term that provides restrictions on the parameter space.