Explain XGBoost Parallelism Strategies

Q: Explain XGBoost Parallelism Strategies

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Question

Explain How XGBoost Parallelizes Training

Scope

Describe how XGBoost achieves parallelism:

Within a single machine
- Histogram-based split finding and why it enables feature- or data-parallel computation
- Handling of sparse features and missing values
- Cache-friendly column/block data layout
- Thread-level work partitioning and reductions
Across multiple machines
- Data-parallel training with all-reduce/ring-reduce
- Synchronization points per tree/level/node
- Determinism and reproducibility considerations
How these choices affect scalability, overfitting, and reproducibility

Explain XGBoost Parallelism Strategies

Explain How XGBoost Parallelizes Training

Scope

Solution (Locked)

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