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You're reading from Apache Spark 2.x Machine Learning Cookbook Over 100 recipes to simplify machine learning model implementations with Spark

Product type Paperback

Published in Sep 2017

Publisher Packt

ISBN-13 9781783551606

Length 666 pages

Edition 1st Edition

Languages

Scala

Tools

Apache Spark

Concepts

Machine Learning

Authors (5):

Broderick Hall

Meenakshi Rajendran

Shuen Mei

Mohammed Guller

Siamak Amirghodsi

+1 more

View More author details

Table of Contents (14) Chapters

Preface

1. Practical Machine Learning with Spark Using Scala FREE CHAPTER

2. Just Enough Linear Algebra for Machine Learning with Spark

3. Spark's Three Data Musketeers for Machine Learning - Perfect Together

4. Common Recipes for Implementing a Robust Machine Learning System

5. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part I

6. Practical Machine Learning with Regression and Classification in Spark 2.0 - Part II

7. Recommendation Engine that Scales with Spark

8. Unsupervised Clustering with Apache Spark 2.0

9. Optimization - Going Down the Hill with Gradient Descent

10. Building Machine Learning Systems with Decision Tree and Ensemble Models

11. Curse of High-Dimensionality in Big Data

12. Implementing Text Analytics with Spark 2.0 ML Library

13. Spark Streaming and Machine Learning Library

Isotonic regression in Apache Spark 2.0

In this recipe, we demonstrate the IsotonicRegression() function in Spark 2.0. The isotonic or monotonic regression is used when order is expected in the data and we want to fit an increasing ordered line (that is, manifest itself as a step function) to a series of observations. The terms isotonic regression (IR) and monotonic regression (MR) are synonymous in literature and can be used interchangeably.

In short, what we are trying to do with the IsotonicRegression() recipe is to provide a better fit versus some of the shortcomings of Naive Bayes and SVM. While they are both powerful classifiers, Naive Bayes lacks a good estimate of P (C | X) and Support Vector Machines (SVM) at best provides only a proxy (can use hyperplane distance), which is not an accurate estimator in some cases.

How to do it...

Go to the website to download the file and save the file into the data path mentioned in the following code blocks. We use the famous Iris data and fit a...