AutoGluon-Tabular：用于结构化数据的稳健且准确的AutoML
AutoGluon-Tabular: Robust and Accurate AutoML for Structured Data
摘要：
我们介绍AutoGluon-Tabular，这是一个开源的AutoML框架，只需要一行Python就可以在未处理的表格数据集（如CSV文件）上训练高度准确的机器学习模型。与现有的AutoML框架主要关注模型/超参数选择不同，AutoGluon-Tabular通过集成多个模型并将其堆叠在多个层中而获得成功。实验表明，我们的多层组合的许多模型提供了更好地利用分配的训练时间比寻找最好的。第二个贡献是对公共和商业AutoML平台的广泛评估，包括TPOT，H2O，AutoWEKA，Auto-sklearn，AutoGluon和Google AutoML Tables。对来自Kaggle和OpenML AutoML Benchmark的50个分类和回归任务的测试表明，AutoGluon更快，更强大，更准确。我们发现，AutoGluon的表现甚至往往优于所有竞争对手的事后诸葛亮组合。在两个流行的Kaggle竞赛中，AutoGluon在对原始数据进行了4小时的训练后，击败了99%的参与数据科学家。
英文摘要：
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.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
