Book Image

Ensemble Machine Learning Cookbook

By : Dipayan Sarkar, Vijayalakshmi Natarajan
Book Image

Ensemble Machine Learning Cookbook

By: Dipayan Sarkar, Vijayalakshmi Natarajan

Overview of this book

Ensemble modeling is an approach used to improve the performance of machine learning models. It combines two or more similar or dissimilar machine learning algorithms to deliver superior intellectual powers. This book will help you to implement popular machine learning algorithms to cover different paradigms of ensemble machine learning such as boosting, bagging, and stacking. The Ensemble Machine Learning Cookbook will start by getting you acquainted with the basics of ensemble techniques and exploratory data analysis. You'll then learn to implement tasks related to statistical and machine learning algorithms to understand the ensemble of multiple heterogeneous algorithms. It will also ensure that you don't miss out on key topics, such as like resampling methods. As you progress, you’ll get a better understanding of bagging, boosting, stacking, and working with the Random Forest algorithm using real-world examples. The book will highlight how these ensemble methods use multiple models to improve machine learning results, as compared to a single model. In the concluding chapters, you'll delve into advanced ensemble models using neural networks, natural language processing, and more. You’ll also be able to implement models such as fraud detection, text categorization, and sentiment analysis. By the end of this book, you'll be able to harness ensemble techniques and the working mechanisms of machine learning algorithms to build intelligent models using individual recipes.
Table of Contents (14 chapters)

Implementing stacked generalization for campaign outcome prediction using H2O

H2O is an open source platform for building machine learning and predictive analytics models. The algorithms are written on H2O's distributed map-reduce framework. With H2O, the data is distributed across nodes, read in parallel, and stored in the memory in a compressed manner. This makes H2O extremely fast.

H2O's stacked ensemble method is an ensemble machine learning algorithm for supervised problems that finds the optimal combination of a collection of predictive algorithms using stacking. H2O's stacked ensemble supports regression, binary classification, and multiclass classification.

In this example, we'll take a look at how to use H2O's stacked ensemble to build a stacking model. We'll use the bank marketing dataset which is available in the Github.