Regulariseerimine

Regulariseerimine is a term often encountered in statistics and machine learning, referring to techniques used to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific characteristics, leading to poor performance on unseen data. Regularisation introduces a penalty term to the model's loss function, discouraging overly complex models.

The most common forms of regularisation are L1 and L2 regularisation, also known as Lasso and Ridge regression respectively. L1 regularisation adds the absolute value of the model's weights to the loss function. This has the effect of shrinking some weights exactly to zero, effectively performing feature selection by removing less important features. L2 regularisation, on the other hand, adds the square of the weights to the loss function. This shrinks weights towards zero but typically does not set them to exactly zero, leading to a model that uses all features but with reduced influence.

Another form of regularisation is dropout, primarily used in neural networks. During training, dropout randomly deactivates a fraction of neurons in each layer, preventing co-adaptation of neurons and forcing the network to learn more robust representations. The choice of regularisation technique and its strength (controlled by a hyperparameter) depends on the specific problem and model. Regularisation is a crucial step in building reliable and generalizable machine learning models.