Book Image

Apache Spark 2.x Machine Learning Cookbook

By : Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall
Book Image

Apache Spark 2.x Machine Learning Cookbook

By: Mohammed Guller, Siamak Amirghodsi, Shuen Mei, Meenakshi Rajendran, Broderick Hall

Overview of this book

Machine learning aims to extract knowledge from data, relying on fundamental concepts in computer science, statistics, probability, and optimization. Learning about algorithms enables a wide range of applications, from everyday tasks such as product recommendations and spam filtering to cutting edge applications such as self-driving cars and personalized medicine. You will gain hands-on experience of applying these principles using Apache Spark, a resilient cluster computing system well suited for large-scale machine learning tasks. This book begins with a quick overview of setting up the necessary IDEs to facilitate the execution of code examples that will be covered in various chapters. It also highlights some key issues developers face while working with machine learning algorithms on the Spark platform. We progress by uncovering the various Spark APIs and the implementation of ML algorithms with developing classification systems, recommendation engines, text analytics, clustering, and learning systems. Toward the final chapters, we’ll focus on building high-end applications and explain various unsupervised methodologies and challenges to tackle when implementing with big data ML systems.
Table of Contents (20 chapters)
Title Page
Credits
About the Authors
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface

Introduction


Decision trees are one of the oldest more widely used methods of machine learning in commerce. What makes them popular is not only their ability to deal with more complex partitioning and segmentation (they are more flexible than linear models) but also their ability to explain how we arrived at a solution and as to "why" the outcome is predicated or classified as a class/label.

Apache Spark provides a good mix of decision tree algorithms fully capable of taking advantage of parallelism in Spark. The implementation ranges from the straight forward Single Decision Tree (the CART type algorithm) to Ensemble Trees, such as Random Forest Trees and GBT (Gradient Boosted Tree). They all have both the variant flavors to facilitate classification (for example, categorical, such as height = short/tall) or regression (for example, continuous, such as height = 2.5 meters).

The following figure depicts a mind map that shows Spark ML library coverage of decision tree algorithms, as at the...