arXiv:2606.26337v1 Announce Type: new Abstract: Gradient Boosted Decision Trees (GBDT), exemplified by LightGBM, spend a dominant fraction of training time -- typically 65-70% -- constructing per-feature histograms. Existing approaches such as random feature subsampling (feature_fraction) discard features without regard for their predictive utility. We propose EMA-based Feature Screening (EMA-FS), an algorithm-level optimization that maintains an exponential moving average (EMA) of per-feature split gains across boosting iterations and, after a short warmup, restricts histogram construction to
The continuous push for more efficient machine learning models and the increasing computational demands of AI development necessitate constant algorithmic optimization.
Improving the efficiency of GBDT training directly impacts the cost and speed of developing and deploying many AI applications, making advanced ML more accessible and scalable.
GBDT models can now be trained significantly faster and with potentially lower computational resources by intelligently screening features, rather than discarding them randomly.
- · AI/ML Developers
- · Cloud Computing Providers
- · SaaS Companies utilizing GBDT
- · Data Scientists
- · Companies with inefficient model training pipelines
- · Hardware providers whose value proposition relies on brute-force compute scaling
Faster iteration and deployment cycles for GBDT-based applications.
Increased adoption of GBDT in areas previously limited by training time, leading to more sophisticated decision-making systems.
Potential reallocation of compute resources from GBDT training to other AI tasks, indirectly accelerating advancements in other ML domains.
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Read at arXiv cs.LG