使用显式参数、指标和工件规划可重现的 ML 实验运行。在模型训练前使用，以标准化可跟踪的实验定义。
Compare model candidates using weighted metrics and deterministic ranking outputs. Use for benchmark leaderboards and model promotion decisions.
Calculates classification and regression metrics like accuracy, F1-score, RMSE, and provides confusion matrix to evaluate ML model performance.
Task automation, containerization, CI/CD, and experiment tracking
Automates training of machine learning models like Random Forest and XGBoost with hyperparameter tuning and export options from provided datasets.
Track and log progress on long-term goals with daily updates, milestone marking, MRR tracking, and weekly summaries.
Cleans and preprocesses datasets for ML by imputing missing values, scaling numeric features, and encoding categorical variables.
