Regularisointimuotoja

Regularisointimuotoja, also known as regularization forms, are mathematical techniques used in various fields, including statistics, machine learning, and optimization, to prevent overfitting and improve the generalization of models. Overfitting occurs when a model learns the noise in the training data rather than the underlying pattern, leading to poor performance on new, unseen data. Regularization methods address this issue by adding a penalty term to the loss function, encouraging the model to have smaller coefficients or simpler structures.

One of the most common regularization techniques is L2 regularization, also known as Ridge regression. This method adds a penalty proportional to the square of the magnitude of coefficients to the loss function. Another popular technique is L1 regularization, or Lasso regression, which adds a penalty proportional to the absolute value of the coefficients. L1 regularization can lead to sparse models, where some coefficients are exactly zero, effectively performing feature selection.

Elastic Net is a combination of L1 and L2 regularization, providing a balance between the two methods. It is particularly useful when dealing with highly correlated features. Dropout is another regularization technique commonly used in neural networks. During training, dropout randomly sets a fraction of the input units to zero at each update, preventing the network from becoming too reliant on any single neuron.

Regularization methods are crucial for building robust and generalizable models. By incorporating regularization into the training process, practitioners can improve the model's performance on unseen data and enhance its overall reliability.