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
There are several common types of regularization. L1 regularization, also known as Lasso, adds a penalty proportional
The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter)