Answer: To prevent overfitting by penalizing model complexity

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

Author: Ritesh Yadav