  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.
