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
By penalizing large coefficients, L2 Regulierung discourages complex models that might be overly sensitive to the