Could you explain the concept of regularization and its impact on machine learning?

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Anonymous · Accepted Answer

regulation is normally used to tackle overfitting issues, both add a penalty to
the model's loss function. There are Lasso regulation vs Ridge regulation. While
Lasso regulation eliminates features with 0 correlation with the target, Ridge
regulations doesn't eliminate any features, it shrinks all coefficients toward 0
but never makes them 0.

Could you explain the concept of regularization and its impact on machine learning?

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