Table of Contents

Preface

Part: 1H2O AutoML Basics

1

Understanding H2O AutoML Basics

Technical requirements

Understanding AutoML and H2O AutoML

AutoML

H2O AutoML

Minimum system requirements to use H2O AutoML

Installing Java

Basic implementation of H2O using Python

Installing Python

Installing H2O using Python

Basic implementation of H2O using R

Installing R

Installing H2O using R

Training your first ML model using H2O AutoML

Understanding the Iris flower dataset

Model training

Summary

2

Working with H2O Flow(H2O’s Web UI)

Technical requirements

Understanding the basics of H2O Flow

Downloading and launching H2O Flow

Exploring H2O Flow

Working with data functions in H2O Flow

Importing the dataset

Parsing the dataset

Observing the dataframe

Splitting a dataframe

Working with model training functions in H2O Flow

Understanding the AutoML parameters in H2O Flow

Training and understanding models using AutoML in H2O Flow

Working with prediction functions in H2O Flow

Making predictions using H2O Flow

Understanding the prediction results

Summary 

Part 2: H2O AutoMLDeep Dive

3

Understanding Data Processing

Technical requirements

Reframing your dataframe

Combining columns from two dataframes

Combining rows from two dataframes

Merging two dataframes

Handling missing values in the dataframe

Filling NA values

Replacing values in a frame

Imputation

Manipulating feature columns of the dataframe

Sorting columns

Changing column types

Tokenization of textual data

Encoding data using target encoding

Summary

4

Understanding H2O AutoML Architecture and Training

Observing the high-level architecture of H2O

Observing the client layer

Observing the JVM component layer

Learning about the flow of interaction between the client and the H2O service

Learning about H2O client-server interactions during the ingestion of data

Knowing the sequence of interactions in H2O during model training

Understanding how H2O AutoML performs hyperparameter optimization and training

Understanding hyperparameters

Understanding hyperparameter optimization

Summary

5

Understanding AutoML Algorithms

Understanding the different types of ML algorithms

Understanding the Generalized Linear Model algorithm

Introduction to linear regression

Understanding the assumptions of linear regression

Working with a Generalized Linear Model

Understanding the Distributed Random Forest algorithm

Introduction to decision trees

Introduction to Random Forest

Understanding Extremely Randomized Trees

Understanding the Gradient Boosting Machine algorithm

Building a Gradient Boosting Machine

Understanding what is Deep Learning

Understanding neural networks

Summary

6

Understanding H2O AutoML Leaderboard and Other Performance Metrics

Exploring the H2O AutoML leaderboard performance metrics

Understanding the mean squared error and the root mean squared error

Working with the confusion matrix

Calculating the receiver operating characteristic and its area under the curve (ROC-AUC)

Calculating the precision-recall curve and its area under the curve (AUC-PR)

Working with log loss

Exploring other model performance metrics

Understanding the F1 score performance metric

Calculating the absolute Matthews correlation coefficient

Measuring the R2 performance metric

Summary

7

Working with Model Explainability

Technical requirements

Working with the model explainability interface

Implementing the model explainability interface in Python

Implementing the model explainability interface in R

Exploring the various explainability features

Understanding residual analysis

Understanding variable importance

Understanding feature importance heatmaps

Understanding model correlation heatmaps

Understanding partial dependency plots

Understanding SHAP summary plots

Understanding individual conditional expectation plots

Understanding learning curve plots

Summary

Part 3: H2O AutoML Advanced Implementation and Productization

8

Exploring Optional Parameters for H2O AutoML

Technical requirements

Experimenting with parameters that support imbalanced classes

Understanding undersampling the majority class

Understanding oversampling the minority class

Working with class balancing parameters in H2O AutoML

Experimenting with parameters that support early stopping

Understanding early stopping

Working with early stopping parameters in H2O AutoML

Experimenting with parameters that support cross-validation

Understanding cross-validation

Working with cross-validation parameters in H2O AutoML

Summary

9

Exploring Miscellaneous Features in H2O AutoML

Technical requirements

Understanding H2O AutoML integration in scikit-learn

Building and installing scikit-learn

Experimenting with scikit-learn

Using H2O AutoML in scikit-learn

Understanding H2O AutoML event logging

Summary

10

Working with Plain Old Java Objects (POJOs)

Technical requirements

Introduction to POJOs

Extracting H2O models as POJOs

Downloading H2O models as POJOs in Python

Downloading H2O models as POJOs in R

Downloading H2O models as POJOs in H2O Flow

Using a H2O model as a POJO

Summary

11

Working with Model Object, Optimized (MOJO)

Technical requirements

Understanding what a MOJO is

Extracting H2O models as MOJOs

Extracting H2O models as MOJOs in Python

Extracting H2O models as MOJOs in R

Extracting H2O models as MOJOs in H2O Flow

Viewing model MOJOs

Using H2O AutoML model MOJOs to make predictions

Summary

12

Working with H2O AutoML and Apache Spark

Technical requirements

Exploring Apache Spark

Understanding the components of Apache Spark

Understanding the Apache Spark architecture

Understanding what a Resilient Distributed Dataset is

Exploring H2O Sparkling Water

Downloading and installing H2O Sparkling Water

Implementing Spark and H2O AutoML using H2O Sparkling Water

Summary

13

Using H2O AutoML with Other Technologies

Technical requirements

Using H2O AutoML and Spring Boot

Understanding the problem statement

Designing the architecture

Working on the implementation

Using H2O AutoML and Apache Storm

What is Apache Storm?

Understanding the problem statement

Designing the architecture

Working on the implementation

Summary

Index

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