How familiar are you with regularization in the context of machine learning?

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Anonymous · Accepted Answer

Regularization is used to reduce overfitting in machine learning model.
Overfitting is an aspect where the model has over learned on trained data and is
too good in regressing and classifying on train data while it's not
generalizable enough on test, validation or holdout data. This usually happens
because when we employ gradient descent or any other optimization algorithm,
they want the error to be 0 and they have succeeded in this aspect. Thus, we
utilize a regularization like L1, L2 or dropout to to penalize the weight
updates due to gradient descent slowing down the process. This penalizes the
model in over-training making the model more generalizable across datasets

How familiar are you with regularization in the context of machine learning?

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