Regularization Parameter

In machine learning, regularization is a technique used to prevent overfitting by adding a penalty term to the loss function. The regularization parameter is a hyperparameter that controls the strength of this penalty term. A higher value of the regularization parameter results in a stronger penalty, which in turn leads to a simpler model that is less likely to overfit. Conversely, a lower value of the regularization parameter results in a weaker penalty, which can lead to a more complex model that is more likely to overfit. The choice of the regularization parameter is typically determined through cross-validation or other hyperparameter tuning techniques.
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