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Apache Spark 2.x Cookbook

You're reading from  Apache Spark 2.x Cookbook

Product type Book
Published in May 2017
Publisher
ISBN-13 9781787127265
Pages 294 pages
Edition 1st Edition
Languages
Author (1):
Rishi Yadav Rishi Yadav
Profile icon Rishi Yadav
Toc

Table of Contents (19) Chapters close

Title Page
Credits
About the Author
About the Reviewer
www.PacktPub.com
Customer Feedback
Preface
1. Getting Started with Apache Spark 2. Developing Applications with Spark 3. Spark SQL 4. Working with External Data Sources 5. Spark Streaming 6. Getting Started with Machine Learning 7. Supervised Learning with MLlib — Regression 8. Supervised Learning with MLlib — Classification 9. Unsupervised Learning 10. Recommendations Using Collaborative Filtering 11. Graph Processing Using GraphX and GraphFrames 12. Optimizations and Performance Tuning

Doing classification using decision trees


Decision trees are the most intuitive among machine-learning algorithms. We use decision trees in our daily lives all the time.

Decision tree algorithms have a lot of useful features:

  • Easy to understand and interpret
  • Work with both categorical and continuous features
  • Work with missing features
  • Do not require feature scaling

Decision tree algorithms work in an upside-down order in which an expression containing a feature is evaluated at every level and this splits the dataset into two categories. We will help you understand this with a simple dumb charades example, which most of us may have played in college. I guessed an animal and asked my coworker to ask me questions to work out my choice. Here's how her questioning went:

  • Q1: Is it a big animal?

Answer: Yes.

  • Q2: Does this animal live for more than 40 years?

Answer: Yes.

  • Q3: Is this animal an elephant?

Answer: Yes.

This is obviously an oversimplified case in which she knew I had postulated an elephant (what...

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