Regularisointitekniikka

Regularisointitekniikka, known in English as regularization, is a set of techniques 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 idiosyncrasies, leading to poor performance on unseen data. Regularization adds a penalty term to the model's objective function, discouraging overly complex models. This penalty is typically proportional to the magnitude of the model's coefficients or weights.

The core idea behind regularization is to introduce a bias to the model to reduce variance. By penalizing large coefficients, regularization effectively shrinks them towards zero, simplifying the model and making it more robust to noise. This leads to a better generalization to new data.

Common types of regularization include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds a penalty proportional to the absolute value of the coefficients, which can drive some coefficients exactly to zero, thus performing feature selection. L2 regularization adds a penalty proportional to the square of the coefficients, which shrinks coefficients towards zero but rarely makes them exactly zero. Elastic Net is a combination of both L1 and L2 regularization.

Regularization is a crucial tool for building reliable predictive models, especially when dealing with high-dimensional data or limited training samples. It helps to find a balance between model fit and model complexity, ultimately improving the model's ability to generalize.