regularisaatioon

Regularisaatio is a technique used in statistics and machine learning to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific details, which leads to poor performance on new, unseen data. Regularisation introduces a penalty term to the model's objective function, discouraging it from becoming too complex.

The core idea is to add a constraint on the model's parameters, typically by penalizing large parameter values. This encourages the model to find simpler solutions that generalize better. Common types of regularisation include L1 (Lasso) and L2 (Ridge) regularisation. L1 regularisation adds a penalty proportional to the absolute value of the weights, which can lead to sparse models where some weights are exactly zero, effectively performing feature selection. L2 regularisation adds a penalty proportional to the square of the weights, which shrinks the weights towards zero but rarely makes them exactly zero.

The choice of regularisation strength, often controlled by a hyperparameter, is crucial. Too little regularisation may not prevent overfitting, while too much can lead to underfitting, where the model is too simple to capture the underlying patterns in the data. Regularisation is a fundamental tool for building robust and reliable machine learning models.