Final answer:
L1 regularization encourages sparse weights, while L2 regularization promotes a more balanced solution.
Step-by-step explanation:
L1 regularization and L2 regularization are two commonly used methods in machine learning to prevent overfitting. L1 regularization adds a penalty term to the cost function that encourages the model to have sparse weights, effectively selecting only the most important features. On the other hand, L2 regularization adds a penalty term that discourages large weights, thereby promoting a more balanced solution.
As an example, consider a linear regression problem with two features: age and income. With L1 regularization, the model may assign a large weight to the income feature and a small weight to the age feature, effectively ignoring the latter. With L2 regularization, the model will likely assign more balanced weights to both features.