What technique helps prevent overfitting in machine learning by constraining model parameters?

Regularization is a technique used in machine learning to prevent overfitting by introducing a penalty for large coefficients in the model. It effectively constrains the model parameters, encouraging simplicity and preventing the model from fitting too closely to the noise in the training data. By applying regularization, one modifies the loss function to include a term that penalizes complex models. Common forms of regularization include L1 (Lasso) and L2 (Ridge) regularization, where L1 adds the absolute value of the coefficients as a penalty and L2 adds the square of the coefficients.

The purpose of this penalty is to discourage the algorithm from assigning too much importance to any single feature, promoting a more balanced model that generalizes better to unseen data. This approach ensures that the model captures the underlying patterns in the data without overreacting to small fluctuations or noise.

Normalization, feature selection, and cross-validation serve different purposes. Normalization adjusts the scale of the data, making it easier for models to converge, but it does not directly prevent overfitting. Feature selection involves choosing a subset of relevant features to improve model performance, which can help with overfitting but doesn't involve constraining parameters. Cross-validation is a technique to assess how the results of