An ML engineer reads that her company's security anomaly detection model uses 'gradient boosting.' A manager asks her how this differs from a neural network for the same task. What is the most accurate practical comparison?

• A.Gradient boosting is a type of neural network with a different activation function
• B.Gradient-boosted decision trees (e.g., XGBoost, LightGBM) are typically more interpretable at the feature importance level, train faster on tabular security data, and require less hyperparameter tuning than deep neural networks - but they are less effective at learning complex hierarchical patterns from high-dimensional unstructured data like raw packet payloads
• C.Gradient boosting always outperforms neural networks on security tasks because it is more recent technology
• D.Neural networks are always more interpretable than gradient boosting because their layers can be visualized