File size: 820 Bytes
3081919 |
1 2 3 4 5 6 |
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. |