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Mastering Machine Learning with Spark 2.x
Mastering Machine Learning with Spark 2.x

Mastering Machine Learning with Spark 2.x: Harness the potential of machine learning, through spark

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Profile Icon Max Pumperla Profile Icon Tellez Profile Icon Malohlava
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$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Aug 2017 340 pages 1st Edition
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$29.99 $43.99
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Arrow left icon
Profile Icon Max Pumperla Profile Icon Tellez Profile Icon Malohlava
Arrow right icon
$19.99 per month
Full star icon Full star icon Full star icon Full star icon Full star icon 5 (1 Ratings)
Paperback Aug 2017 340 pages 1st Edition
eBook
$29.99 $43.99
Paperback
$54.99
Subscription
Free Trial
Renews at $19.99p/m
eBook
$29.99 $43.99
Paperback
$54.99
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Renews at $19.99p/m

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Mastering Machine Learning with Spark 2.x

Detecting Dark Matter - The Higgs-Boson Particle

True or false? Positive or negative? Pass or no pass? User clicks on the ad versus not clicking the ad? If you've ever asked/encountered these questions before then you are already familiar with the concept of binary classification.

At it's core, binary classification - also referred to as binomial classification - attempts to categorize a set of elements into two distinct groups using a classification rule, which in our case, can be a machine learning algorithm. This chapter shows how to deal with it in the context of Spark and big data. We are going to explain and demonstrate:

  • Spark MLlib models for binary classification including decision trees, random forest, and the gradient boosted machine
  • Binary classification support in H2O
  • Searching for the best model in a hyperspace of parameters
  • Evaluation metrics for binomial...

Type I versus type II error

Binary classifiers have intuitive interpretation since they are trying to separate data points into two groups. This sounds simple, but we need to have some notion of measuring the quality of this separation. Furthermore, one important characteristic of a binary classification problem is that, often, the proportion of one group of labels versus the other can be disproportionate. That means the dataset may be imbalanced with respect to one label which necessitates careful interpretation by the data scientist.

Suppose, for example, we are trying to detect the presence of a particular rare disease in a population of 15 million people and we discover that - using a large subset of the population - only 10,000 or 10 million individuals actually carry the disease. Without taking this huge disproportion into consideration, the most naive algorithm would guess...

Spark start and data load

Now it's time to fire up a Spark cluster which will give us all the functionality of Spark while simultaneously allowing us to use H2O algorithms and visualize our data. As always, we must download Spark 2.1 distribution from http://spark.apache.org/downloads.html and declare the execution environment beforehand. For example, if you download spark-2.1.1-bin-hadoop2.6.tgz from the Spark download page, you can prepare the environment in the following way:

tar -xvf spark-2.1.1-bin-hadoop2.6.tgz 
export SPARK_HOME="$(pwd)/spark-2.1.1-bin-hadoop2.6 

When the environment is ready, we can start the interactive Spark shell with Sparkling Water packages and this book package:

export SPARKLING_WATER_VERSION="2.1.12"
export SPARK_PACKAGES=\
"ai.h2o:sparkling-water-core_2.11:${SPARKLING_WATER_VERSION},\
ai.h2o:sparkling-water-repl_2.11:$...

Summary

This chapter was all about the binary classification problem: true or false and, for our example, the signal indicative of the Higgs-Boson or background noise? We have explored four different algorithms: single decision tree, random forest, gradient boosted machine, and DNN. For this exact problem, DNNs are the current world-beaters as the models can continue to train for longer (that is, increase the number of epochs) and more layers can be added (http://papers.nips.cc/paper/5351-searching-for-higgs-boson-decay-modes-with-deep-learning.pdf)

In addition to exploring four algorithms and how to perform a grid-search against many hyper-parameters, we also looked at some important model metrics to help you better differentiate between models and understand ways to define how good is good. Our goal for this chapter was to expose you to a variety of different algorithms and...

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Key benefits

  • • Process and analyze big data in a distributed and scalable way
  • • Write sophisticated Spark pipelines that incorporate elaborate extraction
  • • Build and use regression models to predict flight delays

Description

The purpose of machine learning is to build systems that learn from data. Being able to understand trends and patterns in complex data is critical to success; it is one of the key strategies to unlock growth in the challenging contemporary marketplace today. With the meteoric rise of machine learning, developers are now keen on finding out how can they make their Spark applications smarter. This book gives you access to transform data into actionable knowledge. The book commences by defining machine learning primitives by the MLlib and H2O libraries. You will learn how to use Binary classification to detect the Higgs Boson particle in the huge amount of data produced by CERN particle collider and classify daily health activities using ensemble Methods for Multi-Class Classification. Next, you will solve a typical regression problem involving flight delay predictions and write sophisticated Spark pipelines. You will analyze Twitter data with help of the doc2vec algorithm and K-means clustering. Finally, you will build different pattern mining models using MLlib, perform complex manipulation of DataFrames using Spark and Spark SQL, and deploy your app in a Spark streaming environment.

Who is this book for?

Are you a developer with a background in machine learning and statistics who is feeling limited by the current slow and “small data” machine learning tools? Then this is the book for you! In this book, you will create scalable machine learning applications to power a modern data-driven business using Spark. We assume that you already know the machine learning concepts and algorithms and have Spark up and running (whether on a cluster or locally) and have a basic knowledge of the various libraries contained in Spark.

What you will learn

  • • Use Spark streams to cluster tweets online
  • • Run the PageRank algorithm to compute user influence
  • • Perform complex manipulation of DataFrames using Spark
  • • Define Spark pipelines to compose individual data transformations
  • • Utilize generated models for off-line/on-line prediction
  • • Transfer the learning from an ensemble to a simpler Neural Network
  • • Understand basic graph properties and important graph operations
  • • Use GraphFrames, an extension of DataFrames to graphs, to study graphs using an elegant query language
  • • Use K-means algorithm to cluster movie reviews dataset

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Publication date, Length, Edition, Language, ISBN-13
Publication date : Aug 31, 2017
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781785283451
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Apache
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Product Details

Publication date : Aug 31, 2017
Length: 340 pages
Edition : 1st
Language : English
ISBN-13 : 9781785283451
Vendor :
Apache
Category :
Languages :

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Table of Contents

8 Chapters
Introduction to Large-Scale Machine Learning and Spark Chevron down icon Chevron up icon
Detecting Dark Matter - The Higgs-Boson Particle Chevron down icon Chevron up icon
Ensemble Methods for Multi-Class Classification Chevron down icon Chevron up icon
Predicting Movie Reviews Using NLP and Spark Streaming Chevron down icon Chevron up icon
Word2vec for Prediction and Clustering Chevron down icon Chevron up icon
Extracting Patterns from Clickstream Data Chevron down icon Chevron up icon
Graph Analytics with GraphX Chevron down icon Chevron up icon
Lending Club Loan Prediction Chevron down icon Chevron up icon

Customer reviews

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Canming Oct 21, 2017
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Awesome book to get you started in machine learning.
Amazon Verified review Amazon
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