Regularisointiprosessien

Regularisointiprosessien is a Finnish term that translates to "regularization processes" in English. This concept is commonly encountered in machine learning and statistical modeling. Regularization is a technique used to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and random fluctuations, leading to poor performance on new, unseen data.

The core idea behind regularization is to add a penalty term to the model's objective function. This penalty discourages the model from becoming too complex by penalizing large coefficient values. By doing so, regularization encourages simpler models that generalize better.

There are several common types of regularization. L1 regularization, also known as Lasso, adds a penalty proportional to the absolute value of the coefficients. This can lead to sparse models, where some coefficients are exactly zero, effectively performing feature selection. L2 regularization, known as Ridge, adds a penalty proportional to the square of the coefficients. This shrinks the 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 (often controlled by a hyperparameter) are crucial for achieving optimal model performance. These are typically determined through cross-validation. Regularization is a fundamental tool for building robust and reliable predictive models in various fields.