Regularisierung ist ein entscheidendes Konzept in maschinellem Lernen and statistics aimed at die Generalisierung des Modells zu verbessern by reducing overfitting. Overfitting occurs when a model learns the noise in the Trainingsdaten rather than the actual underlying patterns, leading to poor performance on unseen data.
To combat this, regularization introduces a penalty for complexity in the model’s training process. This is typically done by adding a regularization term to the Verlustfunktion das das Modell zu minimieren versucht. Zwei gängige Arten der Regularisierung sind:
- L1-Regularisierung (Lasso): This method adds the absolute value of the coefficients as a penalty term to the loss function. It can lead to sparse models, meaning some feature weights may become exactly zero, effectively removing them from the model.
- L2-Regularisierung (Ridge): This approach adds the square of the coefficients as a penalty term. It tends to shrink the weights of less important features but does not set them to zero, allowing all features to contribute to the prediction.
By incorporating these penalties, regularization helps to maintain a balance between fitting the training data well and keeping the model simple enough to generalize effectively to neue Daten. The choice between L1 and L2 regularization depends on the specific problem and data characteristics. In practice, regularization is an essential tool for building robust and reliable machine learning models.