Abstract
Regularization is the technique of adding a parameter, λ, to the loss function of a learning algorithm to improve its ability to generalize to new examples by reducing overfitting. The role of the extra regularization parameter is to shrink or to minimize the measure of the weights (or parameters) of the other features in the model.
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© 2019 Ekaba Bisong
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Bisong, E. (2019). Regularization for Linear Models. In: Building Machine Learning and Deep Learning Models on Google Cloud Platform. Apress, Berkeley, CA. https://doi.org/10.1007/978-1-4842-4470-8_21
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DOI: https://doi.org/10.1007/978-1-4842-4470-8_21
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Publisher Name: Apress, Berkeley, CA
Print ISBN: 978-1-4842-4469-2
Online ISBN: 978-1-4842-4470-8
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