regulariseerimisest

Regulariseerimisest is an Estonian term that translates to regularization in English. It is a technique used in statistical modeling and machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and outliers, leading to poor performance on new, 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 coefficients to the loss function. This can lead to sparse models where some coefficients are exactly zero, effectively performing feature selection. L2 regularization, also known as Ridge, adds the squared value of the coefficients to the loss function. This shrinks the coefficients towards zero but rarely makes them exactly zero. Elastic Net regularization combines both L1 and L2 penalties.

The goal of regularization is to improve the generalization ability of a model. By penalizing complexity, regularization helps to create models that are more robust and perform better on data that was not used during training. The strength of the regularization penalty is typically controlled by a hyperparameter, which needs to be tuned, often through cross-validation, to achieve optimal performance. Choosing the right regularization technique and its strength is crucial for building effective predictive models.