ensemble-methods

[ICDE'20] ⚖️ A general, efficient ensemble framework for imbalanced classification. | 泛用，高效，鲁棒的类别不平衡学习框架

AI-CryptoTrader is a state-of-the-art cryptocurrency trading bot that uses ensemble methods to make trading decisions based on multiple sophisticated algorithms. Built with the latest machine learning and data science techniques, AI-CryptoTrader provides a powerful toolset and advanced trading stratgies for maximizing your cryptocurrency profits.

Handwritten digit recognition with MNIST & Keras

A unified framework for tabular, time-series, and multimodal machine learning

🛰️ Production-ready ML system for geomagnetic storm prediction | 98% AUC, 70% recall | Threshold-optimized ensemble with real-time inference | 29-year dataset (1996-2025) | NOAA SWPC operational standards | Complete MLOps pipeline

Experimentation tool for exploring multi-model LLM deliberation + social choice voting

Codes for: "Gradient Boosting Decision Trees on Medical Diagnosis over Tabular Data" (IEEE ICAD 2025)

A financial fraud detection & credit risk scoring system utilizing a variety of techniques

Ensemble Deep Random Vector Functional Link with Skip Connections (edRVFL-SC) No GPU required • 100× faster training

🎯 PSOD: Pseudo-Supervised Outlier Detection library for tabular data. Novel approach using ensemble regression prediction errors as outlier scores. Supports mixed data types, multiple transformations, and comprehensive visualization tools.

Official Implementation of Track2Vec: Fairness Music Recommendation with a GPU-Free Customizable-Driven Framework EvalRS-CIKM-2022

Anomaly Detection Advanced - Professional Python project

Multi-agent orchestrator — route tasks across Claude, Codex, Gemini, agy, opencode. Model selection + escalation + output aggregation.

Python framework for multiple GGUF language models to collaborate on tasks using structured communication patterns, aggregating their outputs into coherent responses.

Predict wine quality (Red/White) using machine learning models (e.g., Random Forest, XGBoost). Classifies wines into Low, Medium, and High based on physicochemical properties. Includes model evaluation.

A regression neural network built from scratch to solve the ML-CUP 2025 challenge. Best Result: 13.75 MEE , This project demonstrates proper machine learning methodology with rigorous 5-fold cross-validation, genetic architecture search, and ensemble optimization. The core model is built with NumPy

Flood prediction regression | R² = 0.869 | LightGBM + feature engineering (Kaggle S4E5)

ML framework for predicting magnetic nanoparticle efficacy using ensemble tree-based regression models with SHAP interpretability analysis.

Python backtesting engine with regime-adaptive ensemble scoring (EMA/RSI/ROC/ATR/Nadaraya-Watson), walk-forward optimization, and Monte Carlo robustness testing.

Churn classification | AUC = 0.914 | greedy ensemble from 75 experiments (Kaggle S6E3)