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Awesome H2O

A curated list of research, applications and projects built using the H2O Machine Learning platform

Here you can see meta information about this topic like the time we last updated this page, the original creator of the awesome list and a link to the original GitHub repository.

Last Update: Dec. 2, 2020, 6:14 a.m.

Thank you h2oai & contributors
View Topic on GitHub:
h2oai/awesome-h2o

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Blog Posts & Tutorials

Books

Research Papers

Comparing the performance of Stacked Ensemble Learning & machine learning algorithms like Random Forest, Decision Tree, Adaboost, Gradient Boost and XGBoost Classifier in Python for Stock Market Trend Prediction.

0
0
70d
n/a

Repository of my thesis "Understanding Random Forests"

471
149
4y 5m
n/a

Jin Sung Jang, Brian D. Juran, Kevin Y. Cunningham, Vinod K. Gupta, Young Min Son, Ju Dong Yang, Ahmad H. Ali, Elizabeth Ann L. Enninga, Jaeyun Sung & Konstantinos N. Lazaridis. (2020)

Steven N. Hart, Eric C. Polley, Hermella Shimelis, Siddhartha Yadav, Fergus J. Couch. (2020)

Peter Gijsbers, Erin LeDell, Sebastien Poirier, Janek Thomas, Berndt Bischl, Joaquin Vanschoren. (2019)

Fernando Itano, Ricardo Pires, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandeza. (2019)

Fernando Itano, Miguel Angelo de Abreu de Sousa, Emilio Del-Moral-Hernandez. (2018)

Gregory B. Auffenberg, Khurshid R. Ghani, Shreyas Ramani, Etiowo Usoro, Brian Denton, Craig Rogers, Benjamin Stockton, David C. Miller, Karandeep Singh. (2018)

Andrés Arévalo, Jaime Niño, German Hernandez, Javier Sandoval, Diego León, Arbey Aragón. (2017)

Jacob W. Kamminga, Helena C. Bisby, Duc V. Le, Nirvana Meratnia, Paul J. M. Havinga. (2017)

Tomislav Hengl, Johan G. B. Leenaars, Keith D. Shepherd, Markus G. Walsh, Gerard B. M. Heuvelink, Tekalign Mamo, Helina Tilahun, Ezra Berkhout, Matthew Cooper, Eric Fegraus, Ichsani Wheeler, Nketia A. Kwabena. (2017)

Laura Acion, Diana Kelmansky, Mark van der Laan, Ethan Sahker, DeShauna Jones, Stephan Arnd. (2017)

Rogério G. Lopes, Rommel N. Carvalho, Marcelo Ladeira, Ricardo S. Carvalho. (2016)

Konstantinos N. Vougas, Thomas Jackson, Alexander Polyzos, Michael Liontos, Elizabeth O. Johnson, Vassilis Georgoulias, Paul Townsend, Jiri Bartek, Vassilis G. Gorgoulis. (2016)

Benchmarks

A minimal benchmark for scalability, speed and accuracy of commonly used open source implementations (R packages, Python scikit-learn, H2O, xgboost, Spark MLlib etc.) of the top machine learning algorithms for binary classification (random forests, gradient boosted trees, deep neural networks etc.).

1.79K
336
1y 106d
MIT

Benchmark of open source deep learning packages in R. Mar 7, 2016

Presentations

Courses

USF Distributed Data System Class Example Repository

1
0
10m
n/a

Materials for STATS 418 - Tools in Data Science course taught in the Master of Applied Statistics at UCLA

124
62
3y 5m
n/a

Materials for GWU DNSC 6279 and DNSC 6290.

215
163
11d
n/a

Utilities

A repository for deploying an AWS EMR cluster and submiting spark jobs on it. Boostrapping by default does inclues pysparkling so one can easily use h2o with python and spark.

5
3
3y 5m
Apache-2.0

Model Wrappers for H2O models

10
2
4m
n/a