Regularization in machine learning is a technique used to prevent overfitting by adding a penalty term to the loss function that discourages overly complex models. This helps the model generalize better to unseen data by controlling model complexity.

Key points about regularization:

• Overfitting prevention: The primary goal is to prevent models from fitting too closely to training data noise
• Complexity penalty: Regularization adds a penalty term (like L1/L2 norms) to the loss function
• Generalization: Helps models perform better on new, unseen data
• Common techniques: L1 regularization (Lasso), L2 regularization (Ridge), and Elastic Net

Why other options are incorrect:

• A: Increasing layers is about model architecture, not regularization
• C: Reducing inference time is about optimization, not regularization's primary goal
• D: Eliminating all bias is impossible and not the goal of regularization