Can you outline what sets DMatrix apart in XGBoost compared to other data structures in similar boosting techniques?

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Anonymous · Accepted Answer

In XGBoost, DMatrix is a highly optimized data structure designed to store and
efficiently process data used for training and prediction.
The key features of DMatrix which makes it unique:
1. Memory efficiency with sparse data - DMatrix stores data in Compressed Column
(CSC) format, skipping missing or zero values reducing both memory usage and
computation time.
2. Quantile based histogram optimization - DMatrix stores pre-processed quantile
sketches of features allowing fast split-finding in trees.
3. Cache aware and distributed design - DMatrix supports block structures
optimized and local caching and also it can be shared across multiple GPU's
4. Integrated metadata - Unlike basic numpy and pandas dataframes, DMatrix
directly includes labels, weights, group info( for ranking task) and base
margins information
5. Precomputation and reuse - Once DMatrix is built, it can be reusd by XGBoost
across multiple rounds of bounding without re-computation

Can you outline what sets DMatrix apart in XGBoost compared to other data structures in similar boosting techniques?

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