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Ensemble Machine Learning Cookbook

You're reading from  Ensemble Machine Learning Cookbook

Product type Book
Published in Jan 2019
Publisher Packt
ISBN-13 9781789136609
Pages 336 pages
Edition 1st Edition
Languages
Authors (2):
Dipayan Sarkar Dipayan Sarkar
Profile icon Dipayan Sarkar
Vijayalakshmi Natarajan Vijayalakshmi Natarajan
Profile icon Vijayalakshmi Natarajan
View More author details
Toc

Table of Contents (14) Chapters close

Preface 1. Get Closer to Your Data 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...
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