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 data and keeping the model parameters small. A higher value of λ increases the penalty for large coefficients, leading to a simpler model with smaller coefficients. This can help to reduce the variance of the model and improve its performance on new data.

Regularization parameters are commonly used in various machine learning algorithms, including linear regression, logistic regression, and neural networks. The choice of regularization parameter is crucial and can significantly impact the performance of the model. Techniques such as cross-validation are often used to select an optimal value for the regularization parameter.

Regularization parameters can be tuned using different methods, such as grid search or random search, to find the best value that minimizes the validation error. The selection of the regularization parameter is a critical step in the model development process, as it directly affects the model's ability to generalize to new data.