Regulárizációval

Regulárizációval is a term used in machine learning and statistics to describe techniques that prevent overfitting. 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. Regulárizációval methods introduce a penalty term to the model's objective function, discouraging overly complex models.

Common regularization techniques include L1 and L2 regularization. L1 regularization, also known as Lasso, adds a penalty proportional to the absolute value of the weights. This encourages sparsity, meaning some weights can become exactly zero, effectively performing feature selection. L2 regularization, or Ridge, adds a penalty proportional to the square of the weights. This shrinks the weights towards zero but rarely makes them exactly zero, leading to a more generalized model.

Dropout is another form of regularization, primarily used in neural networks. During training, dropout randomly sets a fraction of the neurons' outputs to zero at each update. This prevents neurons from co-adapting too much and forces the network to learn more robust representations. Early stopping is a simpler regularization method where training is halted when the model's performance on a validation set begins to degrade, even if performance on the training set is still improving.

The goal of regulárizációval is to find a balance between fitting the training data and maintaining good generalization performance. By controlling model complexity, regularization helps to create models that are more reliable and perform better on real-world tasks. The specific regularization technique and its strength are often hyperparameters that need to be tuned for optimal results.