L2Regulierung

L2 Regulierung, also known as L2 Regularization or Ridge Regression, is a technique used in machine learning to prevent overfitting in models. Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on new, unseen data. L2 Regulierung addresses this by adding a penalty term to the model's cost function. This penalty is proportional to the square of the magnitude of the model's coefficients (weights).

The mathematical formulation of the L2 regularization term is the sum of the squares of all the coefficients, multiplied by a regularization parameter, often denoted by lambda (λ). This lambda controls the strength of the regularization. A higher lambda value imposes a stronger penalty on large coefficients, effectively shrinking them towards zero. This shrinkage discourages the model from assigning excessively large weights to any single feature, promoting a more generalized solution.

By penalizing large coefficients, L2 Regulierung discourages complex models that might be overly sensitive to the training data. It effectively smooths the model's learned function, making it more robust to variations in the input data. Unlike L1 regularization (Lasso), which can drive coefficients exactly to zero and perform feature selection, L2 regularization typically shrinks coefficients towards zero but rarely eliminates them entirely. This makes it suitable when all features are believed to be potentially useful. The choice of lambda is often determined through cross-validation.