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Automatic and Interpretable Machine Learning in R with H2O and LIME (Milan Edition)
This document outlines an agenda for a workshop on automatic and interpretable machine learning using H2O and LIME. The agenda includes an introduction to H2O, AutoML, and LIME, as well as examples of regression and classification modeling. It also covers a real-world use case of applying these techniques to help make multimillion dollar decisions for a baseball team. The document provides information on installing the necessary packages and an overview of the featured machine learning techniques.
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Automatic and Interpretable Machine Learning in R with H2O and LIME (Milan Edition)
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- 2. Agenda 2 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 🍕 🍕 🍕 🍕 🍕 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 3. 3 Time Topics / Tasks 7:30 –7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 mlbench for datasets install.packages(‘mlbench’)
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- 5. 5 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 6. Why? • Most users/organizations can benefit from automatic machine learning pipelines. • Eliminate time wasted on human errors, debugging etc. •Model interpretations is crucial for those who must explain their models to regulators or customers. You will learn … • How to build high quality H2O models (almost) automatically. • How to explain predictions from complex H2O models with LIME. • Bonus: A real use-case that led to multimillion-dollar baseball decisions earlier this year. 6
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- 8. About Me 8 • Before H2O • Water Engineer / EngD Researcher / Matlab Fan Boy (wonder why @matlabulous?) •Discovered R, Python, H2O … never look back again • Data Scientist at Virgin Media (UK), Domino Data Lab (US) • At H2O … • Data Scientist / Evangelist / • Sales Engineer / Solution Architect / • Community Manager … The harsh reality of startup life … • H2O SWAG Photographer #AroundTheWorldWithH2Oai Love H2O? Get some stickers! Barcelona Berlin Brussels Paris Milan (2016)
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- 12. CONFIDENTIAL Gartner names H2Oas Leader with the most completeness of vision • H2O.ai recognized as a technology leader with most completeness of vision • H2O.ai was recognized for the mindshare, partner network and status as a quasi-industry standard for machine learning and AI. • H2O customers gave the highest overall score among all the vendors for sales relationship and account management, customer support (onboarding, troubleshooting, etc.) and overall service and support.
- 13. CONFIDENTIAL Platforms with H2Ointegration H2O + KNIME Talk at KNIME Summit Mar 2017
- 14. Founded 2012, SeriesC in Nov, 2017 Products • Driverless AI – Automated Machine Learning • H2O Open Source Machine Learning • Sparkling Water Mission Democratize AI. Do Good Team ~100 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Offices Mountain View, London, Prague 14 Company Overview
- 15. H2O Products In-Memory, Distributed MachineLearning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters
- 16. H2O Products In-Memory, Distributed MachineLearning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters In-Memory, Distributed Machine Learning Algorithms with H2O Flow GUI This Workshop
- 17. CONFIDENTIAL CONFIDENTIAL Worldwide Recognition in the H2O.ai Community Financial InsuranceMarketingHW Vendors Retail Advisory& Accounting Healthcare “H2O.ai's reference customers gave it the highest overall score for sales relationship and overall service and support” - Gartner MQ 2018 Open Source Community Paying Customers
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- 29. 29 Encourage your friends/ colleagues to give a talk. joe@h2o.ai We sponsor meetups We encourage diversity Source: https://www.maddycoupons.in/blog/yummy-yummy-pizza/ … and more Joe’s Meetups Female Speaker Female Speaker Ratio London Dec 2017 Kasia Kulma 1/3 Amsterdam Feb 2018 Andreea Bejinaru 1/2 London Mar 2018 Cheuk Ting Ho1/3 London Jun 2018 Torgyn Shaikhina 1/4 Overall (so far) = 4/12 = 33.3%
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- 34. Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles •Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations H2O-3 Algorithms Overview Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 34
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- 37. H2O Core CPU CPU CPU ModelBuilding H2O Distributed In-Memory
- 38. H2O Core YARN CPU CPU CPU ModelBuilding H2O Distributed In-Memory SQL NFS S3 Firewall or Cloud
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- 45. Supervised Learning • Generalized Linear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles •Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations H2O-3 Supervised Algorithms in AutoML Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning 45
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- 55. Theory • LIME approximates model locally as logistic or linear model • Repeats process many times •Outputs features that are most important to local models Outcome • Approximate reasoning • Complex models can be interpreted • Neural nets, Random Forest, Ensembles etc. 55 Local Interpretable Model-Agnostic Explanations LIME - How does it work?
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- 57. 57 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example examplesregression_...Rmd 8:30 – 9:00 pm 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 58. Learning from BostonHousing Data Features: No. of rooms, Crime Rate, Age … Median House Value (medv) Predictions H2O Machine Learning Learn the Pattern 58
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- 60. 60 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 9:00 – 9:20 pm Classification Example examplesclassification_...Rmd 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 61. Learning from DiabetesData Features: Pregnant, Age, Glucose … Positive / Negative Predictions H2O Machine Learning Learn the Pattern 61
- 62. 62 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 🍕 🍕 🍕 🍕 🍕 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 63. Why? • Most users/organizations can benefit from automatic machine learning pipelines. • Eliminate time wasted on human errors, debugging etc. •Model interpretations is crucial for those who must explain their models to regulators or customers. You will learn … • How to build high quality H2O models (almost) automatically. • How to explain predictions from complex H2O models with LIME. • Bonus: A real use-case that led to multimillion-dollar baseball decisions earlier this year. 63
- 64. 64 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 🍕 🍕 🍕 🍕 🍕 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
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- 66. 66 Time Topics / Tasks 7:00 –7:30 pm Welcome + Data Hack Italia 7:30 – 7:45 pm Install h2o, lime, mlbench from CRAN slides/code: bit.ly/joe_eRum_2018 7:45 – 8:00 pm Introduction (H2O, AutoML, LIME) 8:00 – 8:30 pm Regression Example 8:30 – 9:00 pm 🍕 🍕 🍕 🍕 🍕 9:00 – 9:20 pm Classification Example 9:20 – 9:30 pm Quick Recap 9:30 – 9:45 pm Real Use-Case: Moneyball 9:45 – 10:00 pm Other H2O News + Q & A
- 67. H2O Products In-Memory, Distributed MachineLearning Algorithms with H2O Flow GUI H2O AI Open Source Engine Integration with Spark Lightning Fast machine learning on GPUs Automatic feature engineering, machine learning and interpretability Secure multi-tenant H2O clusters Lightning Fast machine learning on GPUs
- 68. “Confidential and property of H2O.ai. All rights reserved” Supervised Learning • GeneralizedLinear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms on H2O-3 (CPU) Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning
- 69. “Confidential and property of H2O.ai. All rights reserved” Supervised Learning • GeneralizedLinear Models: Binomial, Gaussian, Gamma, Poisson and Tweedie • Naïve Bayes Statistical Analysis Ensembles • Distributed Random Forest: Classification or regression models • Gradient Boosting Machine: Produces an ensemble of decision trees with increasing refined approximations Deep Neural Networks • Deep learning: Create multi-layer feed forward neural networks starting with an input layer followed by multiple layers of nonlinear transformations Algorithms on H2O4GPU (more to come) Unsupervised Learning • K-means: Partitions observations into k clusters/groups of the same spatial size. Automatically detect optimal k Clustering Dimensionality Reduction • Principal Component Analysis: Linearly transforms correlated variables to independent components • Generalized Low Rank Models: extend the idea of PCA to handle arbitrary data consisting of numerical, Boolean, categorical, and missing data Anomaly Detection • Autoencoders: Find outliers using a nonlinear dimensionality reduction using deep learning
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- 72. Coming Up 72 Date City Talk/ Workshop 25 June Milan H2O + LIME Workshop 28 June Rome H2O Intro + Use Cases Mid-July London Next London Meetup (T.B.C.) Mid-Oct London H2O World London 13-14 Nov London Big Data LDN Keynote by Sri (CEO) + Meetup
- 73. • Organizers & Sponsors 73 Thanks! • Code, Slides & Documents •bit.ly/h2o_meetups • bit.ly/joe_eRum_2018 • docs.h2o.ai • Contact • joe@h2o.ai • @matlabulous • github.com/woobe • Please search/ask questions on Stack Overflow • Use the tag `h2o` (not h2 zero)
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- 75. Supported Formats &Data Sources CSV XLS XLSX ORC* Hive* SVMLight ARFF Parquet Avro 1.8.0* HDFS S3 NFS LOCAL SQL 9Formats 5Sources File type or Folder of Files * 1. only if H2O is running as a Hadoop job * 2. Hive files that are saved in ORC format * 3. without multi-file parsing or column type modification
- 76. Distributed Algorithms • Foundation for In-Memory Distributed Algorithm Calculation - Distributed Data Framesand columnar compression • All algorithms are distributed in H2O: GBM, GLM, DRF, Deep Learning and more. Fine-grained map-reduce iterations. • Only enterprise-grade, open-source distributed algorithms in the market User Benefits Advantageous Foundation • “Out-of-box” functionalities for all algorithms (NO MORE SCRIPTING) and uniform interface across all languages: R, Python, Java • Designed for all sizes of data sets, especially large data • Highly optimized Java code for model exports • In-house expertise for all algorithms Parallel Parse into Distributed Rows Fine Grain Map Reduce Illustration: Scalable Distributed Histogram Calculation for GBM Foundation for Distributed Algorithms 76
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