What is the purpose of L1 and L2 regularization in machine learning models?

L1 and L2 regularization are techniques implemented in machine learning models to address the issue of overfitting, which occurs when a model learns the noise in the training data rather than the underlying patterns. By adding a penalty for larger coefficients in the model, both L1 (Lasso) and L2 (Ridge) regularization reduce the freedom of the model to fit the training data perfectly.

L1 regularization promotes sparsity in the coefficients, encouraging some of them to be exactly zero, which effectively selects a simpler model. L2 regularization, on the other hand, discourages large weights—this prevents any one feature from having too much influence on the model. By constraining the model's complexity through these penalties, regularization techniques help ensure that the model generalizes better to unseen data, leading to improved performance in real-world applications.

Regularization serves as a robust way to balance the trade-off between bias and variance, which is key to achieving a model that accurately captures the underlying data trends without being misled by noise or outliers.