Regularisaation

Regularization 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 patterns, leading to poor performance on new, unseen data. Regularization works by adding a penalty term to the model's objective function, which discourages overly complex models. This penalty is typically a function of the model's parameters (coefficients or weights).

The core idea is to reduce the model's variance at the expense of a slight increase in bias. By constraining the magnitude of the model's parameters, regularization effectively simplifies the model, making it more generalizable. This leads to better performance on data that the model has not encountered during training.

Common types of regularization include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds a penalty proportional to the absolute value of the weights, which can drive some weights to exactly zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the weights, which shrinks weights towards zero but rarely makes them exactly zero. Both methods aim to improve the model's ability to generalize by preventing it from becoming too sensitive to the training data. The choice between L1 and L2, or other regularization techniques, often depends on the specific problem and dataset.