Regulaatioparametri

Regulaatioparametri, often translated as regularization parameter, is a concept used in machine learning and statistics to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data. The regularization parameter controls the strength of the penalty applied to the model's complexity.

In many machine learning algorithms, particularly those involving linear models or neural networks, a cost function is minimized to train the model. This cost function typically has two components: a data-fitting term that measures how well the model explains the data, and a regularization term that penalizes complex models. The regularization parameter, often denoted by lambda (λ) or alpha (α), is a hyperparameter that determines the weight of the regularization term relative to the data-fitting term.

A larger regularization parameter imposes a stronger penalty on complexity, pushing the model towards simpler solutions and reducing the risk of overfitting. Conversely, a smaller parameter allows the model to be more complex, potentially fitting the training data more closely but increasing the chance of overfitting. Choosing the optimal value for the regularization parameter is crucial for achieving good generalization performance and is typically done through techniques like cross-validation. Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization, which apply different penalties based on the magnitude of the model's coefficients.