Regularization is a technique that helps reduce overfitting in machine learning models, thereby improving their generalization performance on unseen data. Simply put, regularization involves adding an extra term to the model's loss function, which is typically related to the model's complexity, with the aim of penalizing overly complex models.
In practical applications, regularization can be implemented in various ways, with the two most common types being:
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L1 Regularization: Also known as Lasso Regression, it works by adding the sum of the absolute values of the weights to the loss function. L1 regularization can drive some weights to zero, thereby achieving feature selection, which is particularly effective for handling high-dimensional datasets.
For example, in a house price prediction model with hundreds or thousands of features, not all features are closely related to the output variable (house price). By applying L1 regularization, the model tends to ignore irrelevant features (weights set to zero), simplifying the model and improving its performance on new data.
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L2 Regularization: Also known as Ridge Regression, it is implemented by adding the sum of the squares of the weights to the loss function. Unlike L1, L2 regularization does not set weights to zero but drives them close to zero, thereby reducing model complexity while still considering all features to some extent.
For instance, when dealing with an image recognition problem involving thousands of pixel inputs, applying L2 regularization helps keep model weights smaller, reducing overfitting risk and enhancing model stability.
The selection of regularization (L1 or L2) and the adjustment of regularization strength (typically controlled by a hyperparameter called the 'regularization parameter') is usually determined based on cross-validation results to ensure robust performance across different datasets. In practice, combining L1 and L2 regularization, known as Elastic Net regularization, aims to leverage the advantages of both methods.