säännöllistämiselle

Säännöllistämiselle refers to the process of regularization in statistics and machine learning. It is a technique used to prevent overfitting, which occurs when a model learns the training data too well, including its noise and outliers, and consequently performs poorly on new, unseen data. Regularization achieves this by adding a penalty term to the model's objective function. This penalty discourages the model from assigning excessively large weights to its parameters.

There are several common types of regularization. L1 regularization, also known as Lasso regularization, adds a penalty proportional to the absolute value of the magnitude of coefficients. This can lead to sparse solutions, effectively setting some coefficients to zero and performing feature selection. L2 regularization, or Ridge regularization, adds a penalty proportional to the square of the magnitude of coefficients. This tends to shrink coefficients towards zero but rarely makes them exactly zero. Elastic Net regularization combines both L1 and L2 penalties.

The choice of regularization technique and the strength of the penalty term (often controlled by a hyperparameter) are crucial. These are typically determined through cross-validation, where the model's performance is evaluated on different subsets of the data to find the optimal settings that generalize well. Regularization is a fundamental tool for building robust and reliable predictive models.