Search icon CANCEL
Subscription
0
Cart icon
Your Cart (0 item)
Close icon
You have no products in your basket yet
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Scala and Spark for Big Data Analytics

You're reading from   Scala and Spark for Big Data Analytics Explore the concepts of functional programming, data streaming, and machine learning

Arrow left icon
Product type Paperback
Published in Jul 2017
Publisher Packt
ISBN-13 9781785280849
Length 796 pages
Edition 1st Edition
Languages
Concepts
Arrow right icon
Authors (2):
Arrow left icon
Sridhar Alla Sridhar Alla
Author Profile Icon Sridhar Alla
Sridhar Alla
Md. Rezaul Karim Md. Rezaul Karim
Author Profile Icon Md. Rezaul Karim
Md. Rezaul Karim
Arrow right icon
View More author details
Toc

Table of Contents (19) Chapters Close

Preface 1. Introduction to Scala FREE CHAPTER 2. Object-Oriented Scala 3. Functional Programming Concepts 4. Collection APIs 5. Tackle Big Data – Spark Comes to the Party 6. Start Working with Spark – REPL and RDDs 7. Special RDD Operations 8. Introduce a Little Structure - Spark SQL 9. Stream Me Up, Scotty - Spark Streaming 10. Everything is Connected - GraphX 11. Learning Machine Learning - Spark MLlib and Spark ML 12. My Name is Bayes, Naive Bayes 13. Time to Put Some Order - Cluster Your Data with Spark MLlib 14. Text Analytics Using Spark ML 15. Spark Tuning 16. Time to Go to ClusterLand - Deploying Spark on a Cluster 17. Testing and Debugging Spark 18. PySpark and SparkR

Naive Bayes

In ML, Naive Bayes (NB) is an example of the probabilistic classifier based on the well-known Bayes' theorem with strong independence assumptions between the features. We will discuss Naive Bayes in detail in this section.

An overview of Bayes' theorem

In probability theory, Bayes' theorem describes the probability of an event based on a prior knowledge of conditions that is related to that certain event. This is a theorem of probability originally stated by the Reverend Thomas Bayes. In other words, it can be seen as a way of understanding how the probability theory is true and affected by a new piece of information. For example, if cancer is related to age, the information about age can be used...

lock icon The rest of the chapter is locked
Register for a free Packt account to unlock a world of extra content!
A free Packt account unlocks extra newsletters, articles, discounted offers, and much more. Start advancing your knowledge today.
Unlock this book and the full library FREE for 7 days
Get unlimited access to 7000+ expert-authored eBooks and videos courses covering every tech area you can think of
Renews at $19.99/month. Cancel anytime
Banner background image