Säännöllistämismenetelmä

Säännöllistämismenetelmä, known in English as regularization, 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 random fluctuations, leading to poor performance on new, unseen data. Regularization adds a penalty term to the model's loss function, which discourages excessively complex models.

The core idea is to introduce a bias to the model to reduce the variance. By penalizing large coefficients, regularization effectively simplifies the model, making it more generalizable. This helps to improve its predictive accuracy on independent datasets.

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 drive some coefficients exactly to zero, performing feature selection as a byproduct. L2 regularization, or Ridge, adds a penalty proportional to the square of the coefficients. This tends to shrink coefficients towards zero but rarely makes them exactly zero. Elastic Net combines both L1 and L2 penalties. Dropout is another form of regularization, primarily used in neural networks, where randomly selected neurons are ignored during training.

The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter) are crucial for achieving optimal performance. Cross-validation is frequently used to tune these hyperparameters. Regularization is a fundamental tool for building robust and reliable predictive models.