Regularizációhoz

Regularizációhoz is a Hungarian term that translates to "regularization" in English. It is a crucial concept in machine learning and statistics, primarily used to prevent overfitting. Overfitting occurs when a model learns the training data too well, including its noise and specific details, leading to poor performance on new, unseen data. Regularization techniques introduce a penalty term to the model's objective function, discouraging it from becoming too complex.

The core idea behind regularization is to add a constraint on the model's parameters. By adding a penalty based on the magnitude of the model's coefficients, regularization encourages simpler models that generalize better. Common regularization methods 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 zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the weights, which shrinks the weights towards zero but rarely makes them exactly zero. The choice between different regularization techniques often depends on the specific problem and dataset. Regularizációhoz is therefore a vital tool for building robust and reliable machine learning models.