May 2018
Intermediate to advanced
576 pages
14h 42m
English
Content preview from Mastering Machine Learning Algorithms
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Ridge
Ridge regularization (also known as Tikhonov regularization) is based on the squared L2-norm of the parameter vector:
This penalty avoids an infinite growth of the parameters (for this reason, it's also known as weight shrinkage), and it's particularly useful when the model is ill-conditioned, or there is multicollinearity, due to the fact that the samples are completely independent (a relatively common condition).
In the following diagram, we see a schematic representation of the Ridge regularization in a bidimensional scenario:
Ridge ...
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