Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Ensemble Machine Learning Cookbook

You're reading from   Ensemble Machine Learning Cookbook Over 35 practical recipes to explore ensemble machine learning techniques using Python

Arrow left icon
Product type Paperback
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Length 336 pages
Edition 1st Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Author Profile Icon Vijayalakshmi Natarajan
Vijayalakshmi Natarajan
Dipayan Sarkar Dipayan Sarkar
Author Profile Icon Dipayan Sarkar
Dipayan Sarkar
Arrow right icon
View More author details
Toc

Table of Contents (14) Chapters Close

Preface 1. Get Closer to Your Data FREE CHAPTER 2. Getting Started with Ensemble Machine Learning 3. Resampling Methods 4. Statistical and Machine Learning Algorithms 5. Bag the Models with Bagging 6. When in Doubt, Use Random Forests 7. Boosting Model Performance with Boosting 8. Blend It with Stacking 9. Homogeneous Ensembles Using Keras 10. Heterogeneous Ensemble Classifiers Using H2O 11. Heterogeneous Ensemble for Text Classification Using NLP 12. Homogenous Ensemble for Multiclass Classification Using Keras 13. Other Books You May Enjoy

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...
lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image