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

Bisecting KMeans, the new kid on the block in Spark 2.0


In this recipe, we will download the glass and try to identify and label each glass using a bisecting KMeans algorithm. The Bisecting KMeans is a hierarchical version of the K-Mean algorithm implemented in Spark using the BisectingKMeans() API. While this algorithm is conceptually like KMeans, it can considerable speed for some use cases where the hierarchical path is present.

The dataset we used for this recipe is the Glass Identification Database. The study of the classification of types of glass was motivated by criminological research. Glass could be considered as evidence if it is correctly identified. The data can be found at NTU (Taiwan), already in LIBSVM format.

How to do it...

  1. We downloaded the prepared data file in LIBSVM from: https://www.csie.ntu.edu.tw/~cjlin/libsvmtools/datasets/multiclass/glass.scale

The dataset contains 11 features and 214 rows.

  1. The original dataset and data dictionary is also available at the UCI website...