säännöllistämisprosessin

Säännöllistämisprosessin, often translated as regularization in English, is a statistical technique used to prevent overfitting in machine learning models. Overfitting occurs when a model learns the training data too well, including its noise and random fluctuations, leading to poor performance on unseen data. Regularization introduces a penalty term to the model's cost function, which discourages overly complex models.

There are several common types of regularization. L1 regularization, also known as Lasso, adds the absolute value of the model's coefficients to the cost function. This type of regularization can drive some coefficients to exactly zero, effectively performing feature selection. L2 regularization, or Ridge, adds the squared value of the coefficients to the cost function. This method shrinks coefficients towards zero but rarely makes them exactly zero, thus preventing any single feature from dominating the model. Elastic Net regularization combines both L1 and L2 penalties.

The strength of the regularization penalty is controlled by a hyperparameter, often denoted by lambda (λ) or alpha (α). This hyperparameter is typically tuned using cross-validation to find the optimal balance between fitting the training data and generalizing to new data. A higher penalty value leads to a simpler model, while a lower value allows for a more complex model.

By adding this penalty, säännöllistämisprosessin helps to improve the generalization ability of machine learning models, making them more robust and reliable in real-world applications. It is a crucial component in building effective predictive models across various domains.