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 random forest for predicting credit card defaults using H2O

H2O is an open source and distributed machine learning platform that allows you to build machine learning models on large datasets. H2O supports both supervised and unsupervised algorithms and is extremely fast, scalable, and easy to implement. H2O's REST API allows us to access all its functionalities from external programs such as R and Python. H2O in Python is designed to be very similar to scikit-learn. At the time of writing this book, the latest version of H2O is H2O v3.

The reason why H2O brought lightning-fast machine learning to enterprises is given by the following explanation:

"H2O's core code is written in Java. Inside H2O, a distributed key/value store is used to access and reference data, models, objects, and so on, across all nodes and machines. The algorithms are implemented...