Regularisaatioparametri
Regularization parameters are coefficients used in machine learning algorithms to prevent overfitting. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor generalization on new, unseen data. Regularization parameters introduce a penalty for complexity in the model, encouraging simpler models that generalize better.
In linear regression, for example, the regularization parameter lambda (λ) controls the trade-off between fitting the training
Regularization parameters are commonly used in various machine learning algorithms, including linear regression, logistic regression, and
Regularization parameters can be tuned using different methods, such as grid search or random search, to find