Papers › AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data

13 Mar 2020arXiv:2003.06505archive 2025-07-28

Nick Erickson, Jonas Mueller, Alexander Shirkov, Hang Zhang, Pedro Larroy, Mu Li, Alexander Smola

We introduce AutoGluon-Tabular, an open-source AutoML framework that requires only a single line of Python to train highly accurate machine learning models on an unprocessed tabular dataset such as a CSV file. Unlike existing AutoML frameworks that primarily focus on model/hyperparameter selection, AutoGluon-Tabular succeeds by ensembling multiple models and stacking them in multiple layers. Experiments reveal that our multi-layer combination of many models offers better use of allocated training time than seeking out the best. A second contribution is an extensive evaluation of public and commercial AutoML platforms including TPOT, H2O, AutoWEKA, auto-sklearn, AutoGluon, and Google AutoML Tables. Tests on a suite of 50 classification and regression tasks from Kaggle and the OpenML AutoML Benchmark reveal that AutoGluon is faster, more robust, and much more accurate. We find that AutoGluon often even outperforms the best-in-hindsight combination of all of its competitors. In two popular Kaggle competitions, AutoGluon beat 99% of the participating data scientists after merely 4h of training on the raw data.

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Innixma/autogluon-benchmarking officialmentioned in papermentioned on GitHubmxnetApache-2.0 report
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compare_frameworks Innixma/autogluon-benchmarking/autogluon_utils/benchmarking/evaluation/evaluate_utils.py official repository unverified Apache-2.0 (permissive) · f0bc3758999604e2 · report
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filter_results Innixma/autogluon-benchmarking/autogluon_utils/benchmarking/evaluation/evaluate_utils.py official repository unverified Apache-2.0 (permissive) · 6235354c9e7a97ef · report
keep_only_valid_datasets Innixma/autogluon-benchmarking/autogluon_utils/benchmarking/evaluation/evaluate_utils.py official repository unverified Apache-2.0 (permissive) · 6cd741ca23acfca5 · report
get_model_file BingLiHanShuang/AutoGluon_IntegrateSimpleFeedforward/autogluon/model_zoo/model_store.py community (archive-listed) unverified Apache-2.0 (permissive) · 9eca9dfdf9ee9469 · report
get_send_warnings_to_logger jianzhnie/AutoTabular/autotabular/smbo.py community (archive-listed) unverified Apache-2.0 (permissive) · 529656492219c432 · report
lambda_handler aws-samples/autogluon-on-aws/5.sagemaker/02_codefree_automl/lambda_function.py community (archive-listed) unverified Apache-2.0 (permissive) · f99caa21863262c6 · report
round_filters BingLiHanShuang/AutoGluon_IntegrateSimpleFeedforward/autogluon/model_zoo/models/utils.py community (archive-listed) unverified Apache-2.0 (permissive) · 5fffdb76be5668e1 · report
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short_hash BingLiHanShuang/AutoGluon_IntegrateSimpleFeedforward/autogluon/model_zoo/model_store.py community (archive-listed) unverified Apache-2.0 (permissive) · 9093f79796af5f6c · report
transform_fn aws-samples/autogluon-on-aws/5.sagemaker/02_codefree_automl/autogluon-tab-with-test.py community (archive-listed) unverified Apache-2.0 (permissive) · 064f3dbb403ea01f · report

Tasks

AutoMLMolecular Property PredictionNeural Architecture Search

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Molecular Property Prediction Tox21 Autogluon ROC-AUC 77.84 #9 of 20 Archive leaderboard report

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