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+This paper introduces Complex Pattern Optimization, a novel technique that enhances gradient boosting for complex data patterns. 
+It demonstrate its effectiveness through comprehensive benchmarking against LightGBM on a synthetically generated dataset containing 
+intricate feature interactions, nonlinear relationships, and high-dimensional mixed feature types. This method consistently achieves 
+superior accuracy by better capturing complex patterns where traditional gradient boosting approaches struggle. The results establish 
+a new state-of-the-art for complex pattern recognition, with promising applications across multiple machine learning domains including 
+traditional ML, deep learning, and time series forecasting. It provide complete dataset generation code for reproducibility and community benchmarking.