Regularisointimenetelmä

Regularisointimenetelmä, known in English as regularization, is a technique used in machine learning and statistics to prevent overfitting. Overfitting occurs when a model learns the training data too well, capturing noise and specific details that do not generalize to new, unseen data. Regularization introduces a penalty term to the model's cost function, which discourages overly complex models.

The core idea behind regularization is to add a constraint on the model's parameters. By penalizing large parameter values, regularization effectively shrinks the coefficients towards zero, making the model simpler and less sensitive to individual data points. This leads to better generalization performance on test data.

Two common types of regularization are L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds the sum of the absolute values of the weights to the cost function, which can drive some weights to exactly zero, performing feature selection. L2 regularization adds the sum of the squared weights to the cost function, which shrinks weights towards zero but rarely makes them exactly zero. The choice between L1 and L2, or other regularization techniques, often depends on the specific problem and dataset. Regularization is a crucial tool for building robust and reliable machine learning models.