L2säännöllistämistä

L2säännöllistämistä, often referred to as L2 regularization or weight decay, is a technique used in machine learning to prevent overfitting in models. It achieves this by adding a penalty term to the model's loss function that is proportional to the square of the magnitude of the model's weights. This penalty discourages the model from learning overly complex relationships that might be specific to the training data but do not generalize well to unseen data.

The mathematical formulation of L2 regularization involves adding a term of the form $\lambda \sum_{i=1}^n w_i^2$ to the original loss function, where $w_i$ are the model's weights and $\lambda$ is a hyperparameter that controls the strength of the regularization. A larger value of $\lambda$ imposes a stronger penalty on large weights, leading to simpler models. During training, the optimization algorithm seeks to minimize this combined loss function.

By pushing weights towards zero, L2 regularization effectively smooths the learned function, making the model less sensitive to individual data points. This can lead to improved performance on validation and test sets. The choice of the regularization parameter $\lambda$ is crucial and is typically determined through cross-validation. While effective, L2 regularization can sometimes lead to underfitting if the regularization strength is too high, meaning the model becomes too simple to capture the underlying patterns in the data.