Regularization is like a safety net for your model.
It helps fix two main problems: overfitting and underfitting. Let’s break it down:
Keeping It Simple: Regularization adds a little extra rule, like or regularization. This rule makes the model avoid being too complicated. A complex model might do great with training data but struggle with new, unseen data (this is called overfitting).
Helping It Learn Better: Regularization encourages the model to pay attention to the most important details. This reduces extra noise and boosts the model's ability to make good predictions on new data (this helps with underfitting).
In simple terms, it’s all about finding that perfect balance!
Regularization is like a safety net for your model.
It helps fix two main problems: overfitting and underfitting. Let’s break it down:
Keeping It Simple: Regularization adds a little extra rule, like or regularization. This rule makes the model avoid being too complicated. A complex model might do great with training data but struggle with new, unseen data (this is called overfitting).
Helping It Learn Better: Regularization encourages the model to pay attention to the most important details. This reduces extra noise and boosts the model's ability to make good predictions on new data (this helps with underfitting).
In simple terms, it’s all about finding that perfect balance!