Regularization error to prevent overfitting. Our goal is

Regularization addresses overfitting problem by penalizing complex
learning algorithm – it selects one complex hypothesis class and adds regularizer function  to empirical error to prevent overfitting.



Our goal is to choose best learning algorithm among many possible
hypothesis space – the regularization parameter can be chosen best   in case of ridge regration or best  in .

We Will Write a Custom Essay Specifically
For You For Only $13.90/page!

order now

If we have large dataset – we can split the dataset into three
different parts:

Training dataset – where we model our functions using different
learning algorithms.

Development or Validation dataset – on this we tune the parameters
on held out dataset (development or validation set.)

dataset – to test best model we have developed on the training dataset.


I'm Harold!

Would you like to get a custom essay? How about receiving a customized one?

Check it out