Choosing the right value for the regularization parameter \( \lambda \) is crucial. Common techniques include cross-validation, where the dataset is divided into training and validation sets. The model is trained on the training set for different values of \( \lambda \) and evaluated on the validation set. The value of \( \lambda \) that minimizes the validation error is chosen as the optimal value.
