If You Can't Explain It to a Six-Year-Old, You Don't Understand It

Automated excerpt

This encourages the model to drive unimportant weights all the way to zero — effectively removing features. L2 regularization adds a penalty equal to the sum of squared weights. Instead of forcing weights to zero, it shrinks all weights toward zero smoothly — no weight gets completely eliminated, but large weights are penalized heavily.

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