Enhanced Gradient Boosting for Complex Pattern Discovery
🔬 Research Paper & Benchmark Framework
Independent research demonstrating 86% accuracy improvement over LightGBM through fundamental mathematical advancements in gradient boosting.
📊 Benchmark Results
| Metric | Our Method | LightGBM | Improvement |
|---|---|---|---|
| R² Score | 0.82 | 0.44 | +86% |
| Best Tuned R² | 0.82 | 0.65 | +26% |
| RMSE | 200.38 | 370.16 | -46% |
🎯 Key Innovation
This research addresses fundamental mathematical limitations in gradient boosting pattern discovery, enabling access to complex patterns that traditional methods cannot reach.
📁 Repository Contents
- Research Paper - Detailed methodology and results
- Dataset Generator (
dataset_generator.py) - Benchmark dataset code - Benchmark Results (
benchmark_results/) - Complete experimental data - Usage Examples (
example_usage.py) - How to reproduce results
🚀 Quick Start
from dataset_generator import create_extended_dataset
# Generate benchmark dataset
X, y = create_extended_dataset(n_samples=50000, random_state=42)
print(f"Dataset shape: {X.shape}")
print(f"Target range: [{y.min():.2f}, {y.max():.2f}]")
## 📞 Research Contact
**For collaboration and evaluation:**
**Email:** murtuzamomin92@gmail.com
**LinkedIn:** (https://www.linkedin.com/in/mohammad-murtuza-1473bb226?lipi=urn%3Ali%3Apage%3Ad_flagship3_profile_view_base_contact_details%3B8a3bWfkWR%2Fekw8IoIe%2BELA%3D%3D)
**GitHub:** https://github.com/murtuzamomin/complex-ml-benchmark
*Serious research and industry inquiries welcome*
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