Säännöllistysp

Säännöllistysp is a Finnish term that translates to "regularization" in English, commonly used in mathematics and statistics. It refers to a set of techniques used to prevent overfitting in statistical models. 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 methods introduce a penalty term into the model's objective function, discouraging overly complex models.

The core idea behind regularization is to add a constraint or penalty to the model's parameters. This penalty discourages large coefficient values, effectively simplifying the model. By doing so, the model becomes less sensitive to the specific training data and generalizes better to new data. Common types of regularization include L1 (Lasso) and L2 (Ridge) regularization. L1 regularization adds a penalty proportional to the absolute value of the coefficients, which can lead to sparsity by driving some coefficients to exactly zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the coefficients, which shrinks all coefficients towards zero but rarely makes them exactly zero.

Regularization is widely applied in machine learning algorithms such as linear regression, logistic regression, and neural networks. It is a crucial tool for building robust and reliable predictive models that perform well in real-world applications where data can be noisy and complex. The choice of regularization technique and its strength often depends on the specific problem and dataset.