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
The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter