Search icon CANCEL
Arrow left icon
Explore Products
Best Sellers
New Releases
Books
Videos
Audiobooks
Learning Hub
Conferences
Free Learning
Arrow right icon
Arrow up icon
GO TO TOP
Machine Learning with Spark

You're reading from   Machine Learning with Spark Develop intelligent, distributed machine learning systems

Arrow left icon
Product type Paperback
Published in Apr 2017
Publisher Packt
ISBN-13 9781785889936
Length 532 pages
Edition 2nd Edition
Languages
Arrow right icon
Authors (2):
Arrow left icon
Manpreet Singh Ghotra Manpreet Singh Ghotra
Author Profile Icon Manpreet Singh Ghotra
Manpreet Singh Ghotra
Rajdeep Dua Rajdeep Dua
Author Profile Icon Rajdeep Dua
Rajdeep Dua
Arrow right icon
View More author details
Toc

Table of Contents (13) Chapters Close

Preface 1. Getting Up and Running with Spark FREE CHAPTER 2. Math for Machine Learning 3. Designing a Machine Learning System 4. Obtaining, Processing, and Preparing Data with Spark 5. Building a Recommendation Engine with Spark 6. Building a Classification Model with Spark 7. Building a Regression Model with Spark 8. Building a Clustering Model with Spark 9. Dimensionality Reduction with Spark 10. Advanced Text Processing with Spark 11. Real-Time Machine Learning with Spark Streaming 12. Pipeline APIs for Spark ML

Gaussian Mixture Model

A mixture model is a probabilistic model of a sub-population within a population. These models are used to make statistical inferences about a sub-population, given the observations of pooled populations.

A Gaussian Mixture Model (GMM) is a mixture model represented as a weighted sum of Gaussian component densities. Its model coefficients are estimated from training data using the iterative Expectation-Maximization (EM) algorithm or Maximum A Posteriori (MAP) estimation from a trained model.

The spark.ml implementation uses the EM algorithm.

It has the following parameters:

  • k: Number of desired clusters
  • convergenceTol: Maximum change in log-likelihood at which one considers convergence achieved
  • maxIterations: Maximum number of iterations to perform without reaching convergence
  • initialModel: Optional starting point from which to start the EM algorithm
(if this parameter is omitted, a random...
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 ₹800/month. Cancel anytime