The gold-standard library for winning machine learning competitions and enterprise-grade tabular data modeling.
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Optimized implementation of gradient-boosted decision trees with regularization (L1/L2) to prevent overfitting.
Parallel processing, out-of-core computation, and hardware optimization for large datasets.
Dominates Kaggle and ML competitions due to its predictive accuracy and flexibility.
Supports Python, R, Java, Scala, and deployment in production environments.
Built-in tools (SHAP, gain-based) to interpret model decisions.
Let’s help you create robust, scalable, and intelligent solutions.
