AutoML自动机器学习

Automated machine learning optimization for structured/tabular data. Runs iterative experiment loop: data analysis - model selection - hyperparameter tuning - evaluation - decision. Supports RandomForest, XGBoost, LightGBM, CatBoost, GBDT, ExtraTrees, HistGB on CPU. Use when user needs to: (1) optimize ML model on tabular data, (2) auto-tune hyperparameters, (3) find best model config for classification/regression, (4) run automated ML experiments. Triggers: automl, auto ml, optimize model, tune model, hyperparameter tuning, model optimization, find best model, ML experiment, auto train