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
There are several common types of regularization terms. L1 regularization, also known as Lasso, adds the sum
The strength of the regularization is controlled by a hyperparameter, often denoted by lambda (λ). This hyperparameter