Reguleriseerimist

Reguleriseerimist, also known as regularization, is a technique used in machine learning to prevent overfitting and improve the generalization of a model. Overfitting occurs when a model learns the training data too well, including its noise and outliers, which can lead to poor performance on new, unseen data. Regularization addresses this issue by adding a penalty to the loss function, encouraging the model to have smaller weights or simpler structures.

There are several common regularization techniques:

1. L1 Regularization (Lasso): Adds the absolute value of the coefficients as a penalty term to the loss function. This can lead to sparse models where some coefficients are exactly zero.

2. L2 Regularization (Ridge): Adds the squared value of the coefficients as a penalty term to the loss function. This tends to distribute error among the coefficients rather than allowing a few to dominate.

3. Elastic Net: Combines both L1 and L2 regularization, providing a balance between sparsity and smoothness.

4. Dropout: A technique used primarily in neural networks where randomly selected neurons are ignored during training. This prevents the network from becoming too reliant on particular neurons.

5. Early Stopping: Monitors the model's performance on a validation set during training and stops the training process when performance starts to degrade. This helps prevent the model from overfitting to the training data.

Regularization is crucial in building robust machine learning models that can perform well on diverse datasets. By incorporating regularization techniques, practitioners can enhance the model's ability to generalize from training data to new, unseen data.