Evaluate Ensemble Models for Bias-Variance, Speed, and Interpretability
Overview
This question evaluates competency in machine learning and deep learning for large-scale recommendation systems, covering ensemble trade-offs (bias–variance, training speed, interpretability), overfitting mitigation, selection of evaluation metrics, transformer adaptation techniques such as LoRA, architecture contrasts (CNN vs RNN vs Transformer), and training stability issues like gradient vanishing/exploding. It is commonly asked to assess reasoning about scalability, model selection, metric alignment, and optimization in production-scale systems, and it tests both conceptual understanding and practical application within the Machine Learning domain.
Large-Scale Recommendation System: Ensembles, Overfitting, Metrics, Architectures, and Optimization
Context
You are designing a large-scale recommendation/ranking model (millions–billions of events, highly imbalanced positives) and must choose and evaluate ensemble models. You also need to understand modern deep architectures and training stability.
Tasks
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Compare Random Forest (RF) vs. XGBoost in terms of:
- Bias–variance trade-off
- Training speed and scalability
- Interpretability
- Define overfitting. List at least three techniques you would apply to reduce it in this recommendation context.
- Describe at least four evaluation metrics you would use, and when each is preferable.
- Explain how LoRA adapts large transformers. Contrast CNN, RNN, and Transformer architectures; include why attention helps with long-range dependencies.
- What causes gradient vanishing/exploding, and how do batch normalization, residual connections, or careful initialization mitigate it?
Solution
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