regularisaatiosta

Regularization is a technique used in machine learning to prevent overfitting, which occurs when a model learns the training data too well, including its noise and outliers, and performs poorly on new, unseen data. Overfitting is particularly common in complex models with many parameters, such as deep neural networks. Regularization introduces additional information or constraints to the learning algorithm, encouraging it to produce simpler models that generalize better to new data.

There are several common regularization techniques:

1. L1 Regularization (Lasso): Adds a penalty equal to the absolute value of the magnitude of coefficients to the loss function. This can lead to sparse models where some coefficients are exactly zero, effectively performing feature selection.

2. L2 Regularization (Ridge): Adds a penalty equal to the squared magnitude of the coefficients to the loss function. This tends to distribute error among a larger number of features, rather than relying too heavily on a few.

3. Elastic Net: Combines L1 and L2 regularization, providing a balance between the two. It is useful when there are multiple features that are correlated with one another.

4. Dropout: Used primarily in neural networks, dropout randomly sets a fraction of input units to zero at each update during training time, which helps prevent units from co-adapting too much.

5. Early Stopping: Monitors the model's performance on a validation set during training and stops the training process when performance starts to degrade, preventing the model from learning the noise in the training data.

Regularization is crucial for building robust machine learning models that perform well on unseen data. By incorporating regularization techniques, practitioners can improve the generalization ability of their models and enhance their overall performance in real-world applications.