Säännöllistämistekijöiden

Säännöllistämistekijät, often translated as regularization terms or coefficients, are a crucial concept in machine learning and statistical modeling. They are additive components incorporated into the cost function of a model during the training process. The primary purpose of säännöllistämistekijät is to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and specific characteristics, leading to poor performance on unseen data.

By adding a penalty term that discourages overly complex models, regularization helps to improve generalization. This penalty is typically proportional to the magnitude of the model's parameters (weights). Models with large weights are considered more complex, and the regularization term penalizes these large values, encouraging the model to find simpler solutions.

Two common types of regularization are L1 and L2 regularization. L1 regularization, also known as Lasso, adds the sum of the absolute values of the weights to the cost function. This can lead to sparse models, where some weights are driven to exactly zero, effectively performing feature selection. L2 regularization, or Ridge, adds the sum of the squared values of the weights. This tends to shrink weights towards zero but rarely makes them exactly zero, leading to models where all features contribute but with reduced influence. The choice between L1 and L2, or a combination of both (Elastic Net), depends on the specific problem and data characteristics. The strength of the regularization is controlled by a hyperparameter, often denoted by lambda (λ), which is tuned to achieve the optimal balance between model fit and generalization.