Book Image

Large Scale Machine Learning with Python

By : Bastiaan Sjardin, Alberto Boschetti
Book Image

Large Scale Machine Learning with Python

By: Bastiaan Sjardin, Alberto Boschetti

Overview of this book

Large Python machine learning projects involve new problems associated with specialized machine learning architectures and designs that many data scientists have yet to tackle. But finding algorithms and designing and building platforms that deal with large sets of data is a growing need. Data scientists have to manage and maintain increasingly complex data projects, and with the rise of big data comes an increasing demand for computational and algorithmic efficiency. Large Scale Machine Learning with Python uncovers a new wave of machine learning algorithms that meet scalability demands together with a high predictive accuracy. Dive into scalable machine learning and the three forms of scalability. Speed up algorithms that can be used on a desktop computer with tips on parallelization and memory allocation. Get to grips with new algorithms that are specifically designed for large projects and can handle bigger files, and learn about machine learning in big data environments. We will also cover the most effective machine learning techniques on a map reduce framework in Hadoop and Spark in Python.
Table of Contents (17 chapters)
Large Scale Machine Learning with Python
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Preface
Index

Chapter 6. Classification and Regression Trees at Scale

In this chapter, we will focus on scalable methods for classification and regression trees. The following topics will be covered:

  • Tips and tricks for fast random forest applications in Scikit-learn

  • Additive random forest models and subsampling

  • GBM gradient boosting

  • XGBoost together with streaming methods

  • Very fast GBM and random forest in H2O

The aim of a decision tree is to learn a series of decision rules to infer the target labels based on the training data. Using a recursive algorithm, the process starts at the tree root and splits the data on the feature that results in the lowest impurity. Currently, the most widely applicable scalable tree-based applications are based on CART. Introduced by Breiman, Friedman Stone, and Ohlson in 1984, CART is an abbreviation of Classification and Regression Trees. CART is different from other decision tree models (such as ID3, C4.5/C5.0, CHAID, and MARS) in two ways. First, CART is applicable to both...