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Without regularization, it's highly likely to be over-fitted as the dimension of the feature space grows (ref. the curse of dimension). Adding a regularization term on top of error function not only pushes the error function towards the origin, but also makes it rounder, resulting in smoother regression functions.
TO-BE
Implement Ridge Regularization
The text was updated successfully, but these errors were encountered:
AS-IS
Without regularization, it's highly likely to be over-fitted as the dimension of the feature space grows (ref. the curse of dimension). Adding a regularization term on top of error function not only pushes the error function towards the origin, but also makes it rounder, resulting in smoother regression functions.
TO-BE
The text was updated successfully, but these errors were encountered: