Regularisierungsterminen

Regularisierungsterminen, often translated as regularization terms or penalties, are a crucial technique in machine learning and statistics aimed at preventing overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific patterns, leading to poor generalization performance on unseen data. Regularization terms are added to the model's objective function, which the learning algorithm aims to minimize.

The core idea is to penalize model complexity. By adding a term that depends on the magnitude of the model's parameters (weights or coefficients), the optimization process is encouraged to find simpler models. Simpler models are less likely to capture spurious correlations in the training data and thus tend to perform better on new data.

There are several common types of regularization terms. L1 regularization, also known as Lasso, adds the sum of the absolute values of the weights to the objective function. This has the effect of driving some weights to exactly zero, performing a form of feature selection. L2 regularization, or Ridge regression, adds the sum of the squared values of the weights. This shrinks weights towards zero but rarely makes them exactly zero, effectively distributing the importance across all features. Elastic Net regularization combines both L1 and L2 penalties.

The strength of the regularization is controlled by a hyperparameter, often denoted by lambda (λ). This hyperparameter is typically tuned using cross-validation to find the optimal balance between fitting the training data and keeping the model simple. Choosing the appropriate regularization technique and its strength is vital for building robust and generalizable machine learning models.