How does regularization affect weights?

1. How does regularization affect weights?

Regularization is a technique used to control model complexity and prevent overfitting by adding a penalty term to the loss function to affect the learning and updating of weights. Regularization can affect the way the weights are learned and the final weight values, thereby improving the generalization ability of the model.

Here are a few ways how regularization affects weights:

1. Update speed of weights: The regularization term introduces an additional penalty term in the loss function, which affects the update speed of weights. Specifically, regularization will force the weights to be updated towards smaller values, thereby reducing the risk of overfitting. When weights are updated, the regularization term guides the weights to move in a direction that reduces both the loss function and the regularization term.

2. Sparseness of weights: One effect of L1 regularization (Lasso regularization) is to make some weights zero, thereby achieving the effect of feature selection. This means that the model will tend to ignore unimportant features, which improves the generalization ability of the model. This is especially useful for high-dimensional data or situations that contain many redundant features.

3. Prevent overfitting: Regularization makes it easier for the model to generalize to new data by limiting the value range of the weights. Overfitting is often because the model is too complex, and regularization can effectively reduce the complexity of the model, thereby reducing