An AutoML framework for classical machine learning algorithms, automating model selection and hyperparameter tuning through Bayesian optimization, portfolio-based meta-learning, and multi-fidelity evaluation using Successive Halving.
benchmarking scikit-learn parallel-computing ray bayesian-optimization openml hyperparameter-tuning automl smac auto-sklearn meta-learning classical-machine-learning successive-halving
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Updated
May 12, 2025 - Python