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Introduction to Machine Learning with H2O and Python

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Jo-fai Chow
Jo-fai Chow

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Introduction to Machine Learning with H2O and Python Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous H2O Tutorial at Analyx 20th April, 2017
Slides and Code Examples: bit.ly/joe_h2o_tutorials 2
About Me • Civil (Water) Engineer • 2010 – 2015 • Consultant (UK) • Utilities • Asset Management • Constrained Optimization • Industrial PhD (UK) • Infrastructure Design Optimization • Machine Learning + Water Engineering • Discovered H2O in 2014 • Data Scientist • 2015 • Virgin Media (UK) • Domino Data Lab (Silicon Valley, US) • 2016 – Present • H2O.ai (Silicon Valley, US) 3
About Me 4
Side Project #1 – Crime Data Visualization 5 https://github.com/woobe/rApps/tree/master/crimemap http://insidebigdata.com/2013/11/30/visualization-week-crimemap/
Side Project #2 – Data Visualization Contest 6 https://github.com/woobe/rugsmaps http://blog.revolutionanalytics.com/2014/08/winner-for-revolution-analytics-user-group-map-contest.html
Side Project #3 7 Developing R Packages for Fun rPlotter (2014)
About Me 8 R + H2O + Domino for Kaggle Guest Blog Post for Domino & H2O (2014) • The Long Story • bit.ly/joe_kaggle_story
Agenda • About H2O.ai • Company • Machine Learning Platform • Tutorial • H2O Python Module • Download & Install • Step-by-Step Examples: • Basic Data Import / Manipulation • Regression & Classification (Basics) • Regression & Classification (Advanced) • Using H2O in the Cloud 9
Agenda • About H2O.ai • Company • Machine Learning Platform • Tutorial • H2O Python Module • Download & Install • Step-by-Step Examples: • Basic Data Import / Manipulation • Regression & Classification (Basics) • Regression & Classification (Advanced) • Using H2O in the Cloud 10 Background Information For beginners As if I am working on Kaggle competitions Short Break
About H2O.ai 11
Company Overview Founded 2011 Venture-backed, debuted in 2012 Products • H2O Open Source In-Memory AI Prediction Engine • Sparkling Water • Steam Mission Operationalize Data Science, and provide a platform for users to build beautiful data products Team 70 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Headquarters Mountain View, CA 12
13 Our Team Joe
Scientific Advisory Council 14
15
0 10000 20000 30000 40000 50000 60000 70000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # H2O Users H2O Community Growth Tremendous Momentum Globally 65,000+ users globally (Sept 2016) • 65,000+ users from ~8,000 companies in 140 countries. Top 5 from: Large User Circle * DATA FROM GOOGLE ANALYTICS EMBEDDED IN THE END USER PRODUCT 16 0 2000 4000 6000 8000 10000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # Companies Using H2O ~8,000+ companies (Sept 2016) +127% +60%
#AroundTheWorldWithH2Oai 17
H2O for Kaggle Competitions 18
H2O for Academic Research 19 http://www.sciencedirect.com/science/article/pii/S0377221716308657 https://arxiv.org/abs/1509.01199
Users In Various Verticals Adore H2O Financial Insurance MarketingTelecom Healthcare 20
21 Joe (2015) http://www.h2o.ai/gartner-magic-quadrant/
22 Check out our website h2o.ai
H2O Machine Learning Platform 23
24
25
H2O Overview 26
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 27
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 28 Import Data from Multiple Sources
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 29 Fast, Scalable & Distributed Compute Engine Written in Java
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 30 Fast, Scalable & Distributed Compute Engine Written in Java
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 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 31
H2O Deep Learning in Action 32
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 33 Multiple Interfaces
H2O + Python 34
H2O + R 35
36 H2O Flow (Web) Interface
HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 37 Export Standalone Models for Production
38 docs.h2o.ai
H2O + Python Tutorial 39
Learning Objectives • Start and connect to a local H2O cluster from Python. • Import data from Python data frames, local files or web. • Perform basic data transformation and exploration. • Train regression and classification models using various H2O machine learning algorithms. • Evaluate models and make predictions. • Improve performance by tuning and stacking. • Connect to H2O cluster in the cloud. 40
41
Install H2O h2o.ai -> Download -> Install in Python 42
43
Start and Connect to a Local H2O Cluster py_01_data_in_h2o.ipynb 44
Local H2O Cluster 45 Import H2O module Start a local H2O cluster nthreads = -1 means using ALL CPU resources
46 Information of Cluster
Importing Data into H2O py_01_data_in_h2o.ipynb 47
48 Import data into H2O cluster (instead of Python’s memory)
49 Directly from data on the web
50 Convert from Pandas to H2O data frame
Basic Data Transformation & Exploration py_02_data_manipulation.ipynb (see notebooks) 51
52 The Classic Titanic Dataset
53 Only two unique values (0 or 1)
54 “enum” is the data type of categorical data in Java Convert numerical to categorical values
55 Only three unique values (1, 2 or 3)
Regression Models (Basics) py_03a_regression_basics.ipynb 56
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 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 57
58 docs.h2o.ai
59 11 Numerical Features Target
60 Define 11 Numerical Features using their Column Names
61 Split dataset so we can measure out-of-bag performance later
62 Basic H2O Usage for GLM
63 Regression Performance – MSE Lower the better
64 Model Summary
65 Evaluate model performance using test set
66
67 API for other ML algorithms
68 API for other ML algorithms
Classification Models (Basics) py_04_classification_basics.ipynb 69
70 Target
71 Convert numerical to categorical values
72 Define features manually Split dataset so we can measure out-of-bag performance later
73 Basic H2O Usage for GLM
Classification Performance – Confusion Matrix 74
Confusion Matrix 75
76 Model Summary
77 Evaluate model performance using test set
78 Predicted Class Probabilities of Each Class
79 API for other ML algorithms
80 API for other ML algorithms
End of Basics Let’s have a break ☺ 81
Regression Models (Tuning) py_03b_regression_grid_search.ipynb 82
Improving Model Performance (Step-by-Step) 83 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lower Mean Square Error = Better Performance
84 Using same dataset and split as previous tutorial
85 Baseline Model Write down MSE on Test set
Improving Model Performance (Step-by-Step) 86 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
87 Manual settings based on experience
Improving Model Performance (Step-by-Step) 88 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
Cross-Validation 89
90 Manual settings based on experience + 5-fold CV
91 Average MSE from 5-fold CV
Improving Model Performance (Step-by-Step) 92 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
Early Stopping 93
94 Search for lowest MSE from 5-fold CV
Improving Model Performance (Step-by-Step) 95 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
Grid Search 96 Combination Parameter 1 Parameter 2 1 0.7 0.7 2 0.7 0.8 3 0.7 0.9 4 0.8 0.7 5 0.8 0.8 6 0.8 0.9 7 0.9 0.7 8 0.9 0.8 9 0.9 0.9
97
98 Sort Results by MSE Best Model on Top Lowest MSE
99 Stopped at 187 trees (automatic)
Improving Model Performance (Step-by-Step) 100 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
101 Expand Search Space Only search for 9 combinations
102 Sort Results by MSE Best Model on Top Lowest MSE
Improving Model Performance (Step-by-Step) 103 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
Regression Models (Ensembles) py_03c_regression_ensembles.ipynb 104
105 https://github.com/h2oai/h2o- meetups/blob/master/2017_02_23_ Metis_SF_Sacked_Ensembles_Deep_ Water/stacked_ensembles_in_h2o_fe b2017.pdf
106 Keep the Best Model after Random Grid Search
107 Keep the Best Model after Random Grid Search
108 Keep the Best Model after Random Grid Search
109 Lowest MSE = Best Performance API for Stacked Ensembles Use the three models from previous steps
Improving Model Performance (Step-by-Step) 110 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lowest MSE = Best Performance
Classification Models (Ensembles) py_04_classification_ensembles.ipynb 111
112 Highest AUC = Best Performance
H2O in the Cloud py_05_h2o_in_the_cloud.ipynb 113
114
115
Recap 116
Learning Objectives • Start and connect to a local H2O cluster from Python. • Import data from Python data frames, local files or web. • Perform basic data transformation and exploration. • Train regression and classification models using various H2O machine learning algorithms. • Evaluate models and make predictions. • Improve performance by tuning and stacking. • Connect to H2O cluster in the cloud. 117
Improving Model Performance (Step-by-Step) 118 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lowest MSE = Best Performance
119
• Our Friends at • Find us at Poznan R Meetup • Today at 6:15 pm • Uniwersytet Ekonomiczny w Poznaniu Centrum Edukacyjne Usług Elektronicznych 120 Thanks! • Code, Slides & Documents • bit.ly/h2o_meetups • 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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Introduction to Machine Learning with H2O and Python

  • 1. Introduction to Machine Learning with H2O and Python Jo-fai (Joe) Chow Data Scientist joe@h2o.ai @matlabulous H2O Tutorial at Analyx 20th April, 2017
  • 2. Slides and Code Examples: bit.ly/joe_h2o_tutorials 2
  • 3. About Me • Civil (Water) Engineer • 2010 – 2015 • Consultant (UK) • Utilities • Asset Management • Constrained Optimization • Industrial PhD (UK) • Infrastructure Design Optimization • Machine Learning + Water Engineering • Discovered H2O in 2014 • Data Scientist • 2015 • Virgin Media (UK) • Domino Data Lab (Silicon Valley, US) • 2016 – Present • H2O.ai (Silicon Valley, US) 3
  • 5. Side Project #1 – Crime Data Visualization 5 https://github.com/woobe/rApps/tree/master/crimemap http://insidebigdata.com/2013/11/30/visualization-week-crimemap/
  • 6. Side Project #2 – Data Visualization Contest 6 https://github.com/woobe/rugsmaps http://blog.revolutionanalytics.com/2014/08/winner-for-revolution-analytics-user-group-map-contest.html
  • 7. Side Project #3 7 Developing R Packages for Fun rPlotter (2014)
  • 8. About Me 8 R + H2O + Domino for Kaggle Guest Blog Post for Domino & H2O (2014) • The Long Story • bit.ly/joe_kaggle_story
  • 9. Agenda • About H2O.ai • Company • Machine Learning Platform • Tutorial • H2O Python Module • Download & Install • Step-by-Step Examples: • Basic Data Import / Manipulation • Regression & Classification (Basics) • Regression & Classification (Advanced) • Using H2O in the Cloud 9
  • 10. Agenda • About H2O.ai • Company • Machine Learning Platform • Tutorial • H2O Python Module • Download & Install • Step-by-Step Examples: • Basic Data Import / Manipulation • Regression & Classification (Basics) • Regression & Classification (Advanced) • Using H2O in the Cloud 10 Background Information For beginners As if I am working on Kaggle competitions Short Break
  • 12. Company Overview Founded 2011 Venture-backed, debuted in 2012 Products • H2O Open Source In-Memory AI Prediction Engine • Sparkling Water • Steam Mission Operationalize Data Science, and provide a platform for users to build beautiful data products Team 70 employees • Distributed Systems Engineers doing Machine Learning • World-class visualization designers Headquarters Mountain View, CA 12
  • 15. 15
  • 16. 0 10000 20000 30000 40000 50000 60000 70000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # H2O Users H2O Community Growth Tremendous Momentum Globally 65,000+ users globally (Sept 2016) • 65,000+ users from ~8,000 companies in 140 countries. Top 5 from: Large User Circle * DATA FROM GOOGLE ANALYTICS EMBEDDED IN THE END USER PRODUCT 16 0 2000 4000 6000 8000 10000 1-Jan-15 1-Jul-15 1-Jan-16 1-Oct-16 # Companies Using H2O ~8,000+ companies (Sept 2016) +127% +60%
  • 18. H2O for Kaggle Competitions 18
  • 19. H2O for Academic Research 19 http://www.sciencedirect.com/science/article/pii/S0377221716308657 https://arxiv.org/abs/1509.01199
  • 20. Users In Various Verticals Adore H2O Financial Insurance MarketingTelecom Healthcare 20
  • 23. H2O Machine Learning Platform 23
  • 24. 24
  • 25. 25
  • 27. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 27
  • 28. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 28 Import Data from Multiple Sources
  • 29. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 29 Fast, Scalable & Distributed Compute Engine Written in Java
  • 30. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 30 Fast, Scalable & Distributed Compute Engine Written in Java
  • 31. 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 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 31
  • 32. H2O Deep Learning in Action 32
  • 33. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 33 Multiple Interfaces
  • 36. 36 H2O Flow (Web) Interface
  • 37. HDFS S3 NFS Distributed In-Memory Load Data Loss-less Compression H2O Compute Engine Production Scoring Environment Exploratory & Descriptive Analysis Feature Engineering & Selection Supervised & Unsupervised Modeling Model Evaluation & Selection Predict Data & Model Storage Model Export: Plain Old Java Object Your Imagination Data Prep Export: Plain Old Java Object Local SQL High Level Architecture 37 Export Standalone Models for Production
  • 39. H2O + Python Tutorial 39
  • 40. Learning Objectives • Start and connect to a local H2O cluster from Python. • Import data from Python data frames, local files or web. • Perform basic data transformation and exploration. • Train regression and classification models using various H2O machine learning algorithms. • Evaluate models and make predictions. • Improve performance by tuning and stacking. • Connect to H2O cluster in the cloud. 40
  • 41. 41
  • 42. Install H2O h2o.ai -> Download -> Install in Python 42
  • 43. 43
  • 44. Start and Connect to a Local H2O Cluster py_01_data_in_h2o.ipynb 44
  • 45. Local H2O Cluster 45 Import H2O module Start a local H2O cluster nthreads = -1 means using ALL CPU resources
  • 47. Importing Data into H2O py_01_data_in_h2o.ipynb 47
  • 48. 48 Import data into H2O cluster (instead of Python’s memory)
  • 49. 49 Directly from data on the web
  • 50. 50 Convert from Pandas to H2O data frame
  • 51. Basic Data Transformation & Exploration py_02_data_manipulation.ipynb (see notebooks) 51
  • 53. 53 Only two unique values (0 or 1)
  • 54. 54 “enum” is the data type of categorical data in Java Convert numerical to categorical values
  • 55. 55 Only three unique values (1, 2 or 3)
  • 57. 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 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 57
  • 60. 60 Define 11 Numerical Features using their Column Names
  • 61. 61 Split dataset so we can measure out-of-bag performance later
  • 63. 63 Regression Performance – MSE Lower the better
  • 66. 66
  • 67. 67 API for other ML algorithms
  • 68. 68 API for other ML algorithms
  • 72. 72 Define features manually Split dataset so we can measure out-of-bag performance later
  • 74. Classification Performance – Confusion Matrix 74
  • 79. 79 API for other ML algorithms
  • 80. 80 API for other ML algorithms
  • 81. End of Basics Let’s have a break ☺ 81
  • 83. Improving Model Performance (Step-by-Step) 83 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lower Mean Square Error = Better Performance
  • 84. 84 Using same dataset and split as previous tutorial
  • 85. 85 Baseline Model Write down MSE on Test set
  • 86. Improving Model Performance (Step-by-Step) 86 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 88. Improving Model Performance (Step-by-Step) 88 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 90. 90 Manual settings based on experience + 5-fold CV
  • 92. Improving Model Performance (Step-by-Step) 92 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 95. Improving Model Performance (Step-by-Step) 95 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 96. Grid Search 96 Combination Parameter 1 Parameter 2 1 0.7 0.7 2 0.7 0.8 3 0.7 0.9 4 0.8 0.7 5 0.8 0.8 6 0.8 0.9 7 0.9 0.7 8 0.9 0.8 9 0.9 0.9
  • 97. 97
  • 98. 98 Sort Results by MSE Best Model on Top Lowest MSE
  • 99. 99 Stopped at 187 trees (automatic)
  • 100. Improving Model Performance (Step-by-Step) 100 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 101. 101 Expand Search Space Only search for 9 combinations
  • 102. 102 Sort Results by MSE Best Model on Top Lowest MSE
  • 103. Improving Model Performance (Step-by-Step) 103 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969
  • 106. 106 Keep the Best Model after Random Grid Search
  • 107. 107 Keep the Best Model after Random Grid Search
  • 108. 108 Keep the Best Model after Random Grid Search
  • 109. 109 Lowest MSE = Best Performance API for Stacked Ensembles Use the three models from previous steps
  • 110. Improving Model Performance (Step-by-Step) 110 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lowest MSE = Best Performance
  • 112. 112 Highest AUC = Best Performance
  • 113. H2O in the Cloud py_05_h2o_in_the_cloud.ipynb 113
  • 114. 114
  • 115. 115
  • 117. Learning Objectives • Start and connect to a local H2O cluster from Python. • Import data from Python data frames, local files or web. • Perform basic data transformation and exploration. • Train regression and classification models using various H2O machine learning algorithms. • Evaluate models and make predictions. • Improve performance by tuning and stacking. • Connect to H2O cluster in the cloud. 117
  • 118. Improving Model Performance (Step-by-Step) 118 Model Settings MSE (CV) MSE (Test) GBM with default settings N/A 0.4551 GBM with manual settings N/A 0.4433 Manual settings + cross-validation 0.4502 0.4433 Manual + CV + early stopping 0.4429 0.4287 CV + early stopping + full grid search 0.4378 0.4196 CV + early stopping + random grid search 0.4227 0.4047 Stacking models from random grid search N/A 0.3969 Lowest MSE = Best Performance
  • 119. 119
  • 120. • Our Friends at • Find us at Poznan R Meetup • Today at 6:15 pm • Uniwersytet Ekonomiczny w Poznaniu Centrum Edukacyjne Usług Elektronicznych 120 Thanks! • Code, Slides & Documents • bit.ly/h2o_meetups • 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)