regulariseerimise

Regulariseerimine, also known as regularization, is a technique used in machine learning and statistics 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 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 types of 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, effectively performing feature selection.

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, reducing their magnitudes.

3. Elastic Net: Combines both L1 and L2 regularization, providing a balance between feature selection and coefficient shrinkage.

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 overfitting.

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 in building robust and generalizable models, especially when dealing with high-dimensional data or complex models with many parameters. By incorporating regularization techniques, practitioners can enhance the model's ability to generalize to new data and improve its overall performance.