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

Naive Bayes machine learning with Spark 2.0 MLlib


In this recipe, we use the famous Iris dataset and use Spark API NaiveBayes() to classify/predict which of the three classes of flower a given set of observations belongs to. This is an example of a multi-class classifier and requires multi-class metrics for measurements of fit. The previous recipe a binary classification and metric to measure the fit.

How to do it...

  1. For the Naive Bayes exercise, we use a famous dataset called iris.data, which can be obtained from UCI. The dataset was originally introduced in the 1930s by R. Fisher. The set is a multivariate dataset with flower attribute measurements classified into three groups.

In short, by measuring four columns, we attempt to classify a species into one of the three classes of Iris flower (that is, Iris Setosa, Iris Versicolor, Iris Virginica).

We can download the data from here:

https://archive.ics.uci.edu/ml/datasets/Iris/

The column definition is as follows:

    • Sepal length in cm
    • Sepal width...