regulaariseerimisega

Regulaariseerimisega refers to regularization in machine learning. It is a technique used to prevent overfitting, a common problem where a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data. Regularization introduces a penalty term into the model's objective function, which discourages overly complex models.

There are several common types of regularization. L1 regularization, also known as Lasso regression, adds a penalty proportional to the absolute value of the model's coefficients. This encourages sparsity, meaning some coefficients can become exactly zero, effectively performing feature selection. L2 regularization, or Ridge regression, adds a penalty proportional to the square of the model's coefficients. This shrinks coefficients towards zero but rarely makes them exactly zero, leading to a more generalized model without eliminating features entirely. Elastic Net regularization combines both L1 and L2 penalties.

The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter like lambda) are crucial. These are typically tuned using cross-validation to find the optimal balance between fitting the training data and generalizing to new data. Regularization is a fundamental aspect of building robust and reliable machine learning models.