regulárizációt

Regulárizáció is a Hungarian term that translates to regularization in English, a concept widely used in statistics and machine learning. It refers to techniques used to prevent overfitting, a phenomenon where a model learns the training data too well, including its noise and outliers, leading to poor performance on unseen data.

The core idea behind regularization is to add a penalty term to the model's cost function. This penalty discourages overly complex models by imposing a constraint on the model's parameters. By penalizing large parameter values, regularization encourages simpler models that are more likely to generalize well.

Common regularization techniques include L1 regularization (Lasso) and L2 regularization (Ridge). L1 regularization adds a penalty proportional to the absolute value of the weights, which can lead to sparse solutions where some weights are exactly zero, effectively performing feature selection. L2 regularization adds a penalty proportional to the square of the weights, which tends to shrink all weights towards zero without making them exactly zero.

The choice of regularization technique and the strength of the penalty (often controlled by a hyperparameter) are crucial for achieving optimal model performance. These techniques are essential tools for building robust and reliable predictive models.