Weight Decay

Description: Weight decay is a regularization technique used in machine learning that penalizes large weights in a model. Its main goal is to prevent overfitting, a phenomenon where a model fits too closely to the training data, losing its ability to generalize to new data. This technique is implemented by adding a penalty term to the model’s loss function, which is based on the magnitude of the weights. By doing so, it encourages the model to maintain smaller and, therefore, simpler weights. This not only helps improve generalization but can also make the model more interpretable. Weight decay can be applied in various learning algorithms, including linear regression, neural networks, and support vector machines. It is particularly relevant in contexts where data is scarce or noisy, as it helps stabilize learning and prevents the model from fitting spurious patterns in the data. In summary, weight decay is an essential tool in the arsenal of regularization techniques, contributing to the creation of more robust and effective models in machine learning.

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