L1 & L2 Regularization for Preventing Model Overfitting
As we learned in the previous lesson, Regularization helps prevent models from memorizing unnecessary information, allowing them to learn more general patterns.
L1 Regularization and L2 Regularization are representative regularization methods, and in this lesson, we will explore how each technique operates.
L1 Regularization (Lasso Regularization)
L1 Regularization works by automatically removing unnecessary variables, setting the weights of irrelevant features to 0 as the model learns the data.
As a result, the model simplifies and uses only the essential information.
Particularly when there are too many features in the data, L1 regularization is useful for improving model performance by retaining only the most important features and eliminating the rest.
L2 Regularization (Ridge Regularization)
L2 Regularization adjusts all weights to maintain an appropriate size.
When a model learns, if certain features are perceived as excessively important and the weights become too large, it can lead to an over-reliance on specific patterns.
To prevent this, L2 regularization keeps all weights from becoming too large or small, maintaining balance.
L2 regularization does not completely discard specific features but adjusts all features to enable the model to learn more general patterns.
This is effective when all features are important but need to be adjusted to the appropriate level.
In the next lesson, we will explore Transfer Learning, utilizing previously learned models to solve new problems.
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