Scalable Automatic Machine Learning with H2O
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The document provides an overview of H2O.ai and its open-source machine learning platform, which supports various programming interfaces and distributed algorithms for big data. It details the features of H2O's automatic machine learning (AutoML) capabilities, including data preparation, model generation, and the use of stacked ensembles. Additionally, it offers resources for learning H2O AutoML and encourages community contributions.
Scalable Automatic Machine Learning with H2O
- 1. Scalable Automatic Machine Learning with H2O Parul Pandey Data Science Evangelist, H2O.ai
- 2. What is H2O? H2O.ai, the company H2O, the platform • • • Founded in 2012 Advised by Stanford Professors Hastie, Tibshirani & Boyd Headquarters: Mountain View, California, USA • • • Open Source Software (Apache 2.0 Licensed) R, Python, Scala, Java and Web Interfaces Distributed Machine Learning Algorithms for Big Data
- 3. H2OTools
- 5. Agenda • H2O Platform • Automatic Machine Learning (AutoML) • H2O AutoML Overview • Demo
- 6. H2O Platform
- 7. H2O Machine Learning Platform • Open source, distributed (multi-core + multi-node) implementations of cutting edge ML algorithms. • Core algorithms written in high performance Java. • APIs available in R, Python, Scala; web GUI. • Easily deploy models to production as pure Java code. • Works on Hadoop, Spark, AWS, your laptop, etc.
- 8. H2O Machine Learning Features • Supervised & unsupervised machine learning algos (GBM, RF,DNN, GLM, Stacked Ensembles, etc.) • Imputation, normalization & auto one-hot-encoding • Automatic early stopping • Cross-validation, grid search & random search • Variable importance, model evaluation metrics, plots
- 9. Intro to A utomatic Machine Learning
- 10. Aspects of Automatic Machine Learning Data Prep Model Generation Ensembles
- 11. H2O’s Auto ML
- 13. H2O AutoML • Basic data pre-processing (as in all H2O algos). • Trains a Random grid of algorithms like GBMs, DNNs, GLMs, etc. using a carefully chosen hyper-parameter space. • Individual models are tuned using cross-validation. • Two Stacked Ensembles are trained (“All Models” ensemble & a lightweight “Best of Family” ensemble). • Returns a sorted “Leaderboard” of all models. • All models can be easily exported to production.
- 15. Random G r id Search & Stacking • Random Grid Search combined with Stacked Ensembles is a powerful combination. • Ensembles perform particularly well if the models they are based on (1) are individually strong, and (2) make uncorrelated errors. • Stacking usesa second-level metalearning algorithm to find the optimal combination of base learners.
- 16. Who is it for?
- 17. H 2 O A utoML in R
- 18. H2O AutoML in Python
- 19. H 2 O A utoML in Flow GUI
- 20. H 2 O A utoML Leaderboard Example Leaderboard for binary classification
- 21. H2O Auto ML Tutorial
- 22. Learn H2O AutoML! • Docs: https://tinyurl.com/h2o-automl-docs • R& Py tutorials:https://tinyurl.com/h2o-automl-tutorials • Blog: A Deep dive into H2O’s AutoML
- 23. H2O Resources • Documentation: http://docs.h2o.ai • Tutorials: https://github.com/h2oai/h2o-tutorials • Slidedecks: https://github.com/h2oai/h2o-meetups • Videos: https://www.youtube.com/user/0xdata • Stack Overflow: https://stackoverflow.com/tags/h2o • Google Group: https://tinyurl.com/h2ostream • Gitter: http://gitter.im/h2oai/h2o-3 • Events & Meetups: http://h2o.ai/events
- 24. Contribute to H2O! Get in touch over email, Gitter or JIRA. https://github.com/h2oai/h2o-3/blob/master/CONTRIBUTING.md
- 25. Thank you!