A high-performance gradient boosting framework designed for speed and scalability with minimal resources.
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Uses histogram-based algorithms and leaf-wise growth for faster training with lower memory usage.
Supports GPU training for 10x speedups compared to CPU-only libraries.
Handles large-scale datasets (millions of rows) with distributed learning.
Works well with default settings, reducing configuration overhead.
Integrates with Azure ML, .NET, and other Microsoft tools.
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