tabular-ml-modeling

功能与用途

State-of-the-art machine learning for large-scale tabular data (millions of rows, thousands of features). Use when building predictive models on tabular/structured data including regression and classification tasks. Covers GPU-accelerated gradient boosting (XGBoost, LightGBM, CatBoost), RAPIDS acceleration (cuDF, cuML), validation strategies, feature engineering, ensembling (hill climbing, stacking), and competition-winning techniques. Includes Numerai-specific optimizations with deep model parameters, era-based validation, multi-target ensembling, and payout scoring. Optimized for NVIDIA A100 GPU with CUDA 12.4.

快速开始

请根据来源仓库和你的 AI 工具说明安装此技能。先阅读技能文件和依赖要求，再添加到工作流。