regulariseringsparametere

Regulariseringsparametere, also known as regularization parameters, are hyperparameters used in machine learning models to prevent overfitting and improve generalization. Overfitting occurs when a model learns the training data too well, including its noise and outliers, which can lead to poor performance on new, unseen data. Regularization techniques add a penalty to the loss function to constrain or regularize the model parameters, encouraging simpler models that generalize better.

Common regularization techniques include L1 regularization (Lasso), which adds the absolute value of the coefficients as a penalty term, and L2 regularization (Ridge), which adds the squared value of the coefficients. Elastic Net combines both L1 and L2 penalties. Dropout, used primarily in neural networks, randomly sets a fraction of input units to zero during training, preventing co-adaptation of hidden units.

The regularization parameter, often denoted by lambda (λ), controls the strength of the penalty. A smaller λ allows the model to fit the training data more closely, while a larger λ imposes a stronger penalty, leading to simpler models. The optimal value of λ is typically determined through cross-validation, where the model is trained and evaluated on different subsets of the data to find the λ that minimizes the validation error.

Regularization parameters are crucial for building robust machine learning models that perform well on both training and test data. By carefully tuning these parameters, practitioners can enhance model performance and ensure that their models generalize effectively to new data.