👋 This repository contains the code for the H2O AutoML paper, "H2O AutoML: Scalable Automatic Machine Learning", an accepted paper at the ICML 2020 AutoML Workshop.

The Experiments section contains the H2O AutoML specific experiments and the OpenML AutoML Benchmark contains benchmarks against other AutoML systems.

• Hardware & Software: The "Baseline" and "Blending vs CV Stacking" experiments can be replicated using H2O 3.30.0.3 R package on a c5.metal Amazon EC2 instance (96 vCPUs, 192G RAM). An AMI called "h2oautoml_gpu" is available in us-east-1 for convenience. The experiments require R and a few other R package dependencies which can be installed using ./utils/install.sh.
• Data: To run the experiments, first navigate to the ./data/airlines subfolder and download the Airlines data files by running get-data.sh.
• Experiments: Navigate to the experiment code subfolder (e.g. blending_stacking/code) and then run the run-airlines-binary.sh scripts to launch the experiments.

Baseline H2O AutoML (default settings).

Using various subsets of the Airlines Dataset (~150M rows):

• 10k, 100k, 1M, 10M, 100M row training sets
• 100k test set

Compare the baseline (5-fold CV) Stacked Ensemble with 10% blending frame Stacked Ensemble.

Using various subsets of the Airlines Dataset (~150M rows):

• 10k, 100k, 1M, 10M, 100M row training sets
• 100k test set

An overview of the OpenML AutoML Benchmark as well as instructions for how to reproduce the benchmark are available in a separate README.md. Also included in the ./openml_automlbenchmark subfolder is the results files for each framework that was included (TPOT, auto-sklearn, H2O AutoML, AutoGluon-Tabular) and the H2O AutoML leaderboards generated during the benchmark.