Imagine you're trying to build the tallest tower using blocks, L1 and L2 regularization are like two different rules that help you decide how many blocks to use from each pile.
L1 Regularization is like a rule that says: you can only use one block from each pile, no matter how tall your tower gets. This makes your tower simpler because it limits the number of piles you can use. It's kind of like choosing only your favorite toys to build with, fewer choices mean easier decisions.
L2 Regularization is more like a rule that says: you can use as many blocks from each pile as you want, but you have to keep them all in check, if you take too many from one pile, it might make the tower wobbly. This encourages you to spread out your block usage across piles, making the tower more balanced.
If L1 is like picking only your favorite toys, L2 is like sharing your toys evenly so everyone gets a fair chance to help build the tower. Both rules help keep things simple or stable, just in different ways! Imagine you're trying to build the tallest tower using blocks, L1 and L2 regularization are like two different rules that help you decide how many blocks to use from each pile.
L1 Regularization is like a rule that says: you can only use one block from each pile, no matter how tall your tower gets. This makes your tower simpler because it limits the number of piles you can use. It's kind of like choosing only your favorite toys to build with, fewer choices mean easier decisions.
L2 Regularization is more like a rule that says: you can use as many blocks from each pile as you want, but you have to keep them all in check, if you take too many from one pile, it might make the tower wobbly. This encourages you to spread out your block usage across piles, making the tower more balanced.
If L1 is like picking only your favorite toys, L2 is like sharing your toys evenly so everyone gets a fair chance to help build the tower. Both rules help keep things simple or stable, just in different ways!
Examples
- A kid learns to solve math problems by either counting all the steps or just adding up a few big numbers.
- L1 regularization is like choosing only the most important features in a puzzle, while L2 keeps all but makes them smaller.
- Imagine trying to fit a jacket on a person, L1 might remove some buttons entirely, while L2 just loosens them.
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See also
- How Does Regularization Work?
- What is Principal Component Analysis (PCA)?
- What is Overfitting?
- What is regularization?
- What is Gaussian Mixture Models (GMMs)?