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Scala for Machine Learning
Scala for Machine Learning

Scala for Machine Learning: Leverage Scala and Machine Learning to construct and study systems that can learn from data

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Scala for Machine Learning

Chapter 2. Hello World!

In the first chapter, you were acquainted with some rudimentary concepts regarding data processing, clustering, and classification. This chapter is dedicated to the creation and maintenance of a flexible end-to-end workflow to train and classify data. The first section of the chapter introduces a data-centric (functional) approach to create number-crunching applications.

You will learn how to:

  • Apply the concept of monadic design to create dynamic workflows
  • Leverage some of Scala's advanced functional features, such as dependency injection, to build portable computational workflows
  • Take into account the bias-variance trade-off in selecting a model
  • Overcome overfitting in modeling
  • Break down data into training, test, and validation sets
  • Implement model validation in Scala using precision, recall, and F score

Modeling

Data is the lifeline of any scientist, and the selection of data providers is critical in developing or evaluating any statistical inference or machine learning algorithm.

A model by any other name

We briefly introduced the concept of a model in the Model categorization section in Chapter 1, Getting Started.

What constitutes a model? Wikipedia provides a reasonably good definition of a model as understood by scientists [2:1]:

A scientific model seeks to represent empirical objects, phenomena, and physical processes in a logical and objective way.

Models that are rendered in software allow scientists to leverage computational power to simulate, visualize, manipulate and gain intuition about the entity, phenomenon, or process being represented.

In statistics and the probabilistic theory, a model describes data that one might observe from a system to express any form of uncertainty and noise. A model allows us to infer rules, make predictions, and learn from data.

A model is composed...

Designing a workflow

A data scientist has many options in selecting and implementing a classification or clustering algorithm.

Firstly, a mathematical or statistical model is to be selected to extract knowledge from the raw input data or the output of a data upstream transformation. The selection of the model is constrained by the following parameters:

  • Business requirements such as accuracy of results
  • Availability of training data and algorithms
  • Access to a domain or subject-matter expert

Secondly, the engineer has to select a computational and deployment framework suitable for the amount of data to be processed. The computational context is to be defined by the following parameters:

  • Available resources such as machines, CPU, memory, or I/O bandwidth
  • Implementation strategy such as iterative versus recursive computation or caching
  • Requirements for the responsiveness of the overall process such as duration of computation or display of intermediate results

The following diagram illustrates the selection...

Assessing a model

Evaluating a model is an essential part of the workflow. There is no point in creating the most sophisticated model if you do not have the tools to assess its quality. The validation process consists of defining some quantitative reliability criteria, setting a strategy such as an N-Fold cross-validation scheme, and selecting the appropriate labeled data.

Validation

The purpose of this section is to create a Scala class to be used in future chapters for validating models. For starters, the validation process relies on a set of metrics to quantify the fitness of a model generated through training.

Key metrics

Let's consider a simple classification model with two classes defined as positive (with respect to negative) represented with Black (with respect to White) color in the following diagram. Data scientists use the following terminology:

  • True positives (TP): These are observations that are correctly labeled as belonging to the positive class (white dots on a dark background...

Summary

In this chapter, we established the framework for the different data processing units that will be introduced in this book. There is a very good reason why the topics of model validation and overfitting are explored early on in this book. There is no point in building models and selecting algorithms if we do not have a methodology to evaluate their relative merits.

In this chapter, you were introduced to:

  • The versatility and cleanness of the Cake pattern in Scala as an effective scaffolding tool for data processing
  • The concept of pipe operator for data conversion
  • A robust methodology to validate machine learning models
  • The challenge in fitting models to both training and real-world data

The next chapter will address the problem of overfitting by penalizing outliers, modeling, and eliminating noise in data.

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Description

Are you curious about AI? All you need is a good understanding of the Scala programming language, a basic knowledge of statistics, a keen interest in Big Data processing, and this book!

What you will learn

  • Build dynamic workflows for scientific computing
  • Leverage open source libraries to extract patterns from time series
  • Write your own classification, clustering, or evolutionary algorithm
  • Perform relative performance tuning and evaluation of Spark
  • Master probabilistic models for sequential data
  • Experiment with advanced techniques such as regularization and kernelization
  • Solve big data problems with Scala parallel collections, Akka actors, and Apache Spark clusters
  • Apply key learning strategies to a technical analysis of financial markets

Product Details

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Publication date : Dec 17, 2014
Length: 624 pages
Edition : 1st
Language : English
ISBN-13 : 9781783558759
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Publication date : Dec 17, 2014
Length: 624 pages
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Language : English
ISBN-13 : 9781783558759
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Table of Contents

14 Chapters
1. Getting Started Chevron down icon Chevron up icon
2. Hello World! Chevron down icon Chevron up icon
3. Data Preprocessing Chevron down icon Chevron up icon
4. Unsupervised Learning Chevron down icon Chevron up icon
5. Naïve Bayes Classifiers Chevron down icon Chevron up icon
6. Regression and Regularization Chevron down icon Chevron up icon
7. Sequential Data Models Chevron down icon Chevron up icon
8. Kernel Models and Support Vector Machines Chevron down icon Chevron up icon
9. Artificial Neural Networks Chevron down icon Chevron up icon
10. Genetic Algorithms Chevron down icon Chevron up icon
11. Reinforcement Learning Chevron down icon Chevron up icon
12. Scalable Frameworks Chevron down icon Chevron up icon
A. Basic Concepts Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

Customer reviews

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Rating distribution
Full star icon Full star icon Full star icon Half star icon Empty star icon 3.8
(12 Ratings)
5 star 41.7%
4 star 16.7%
3 star 25%
2 star 8.3%
1 star 8.3%
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matej fandl Feb 09, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Studying machine learning during my university times and being an aspiring scala developer I picked this book up as an opportunity to learn scala while reading about what interests me the most in the field of computer science. This wont be an easy read for people not familiar with scala at all, but if you have some experience with the language and are interested in machine learning, I definitely recommend the book. It is a nice and quite deep dive into the topic. What I found very interesting was the optional math part available in each section. This book is also showing me where my understanding of scala is still superficial, the code is written a very good way.
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Amazon Customer Feb 20, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Some technical books are heavy on theory, some are heavy on practicality. There are books that describe ‘why’ and others that show you ‘how’. Personally, I have always tended towards the ‘how’ side of things - the ‘cookbook’ approach is one I have always liked. The more practical the better.Scala For Machine Learning (full disclosure, I received an unpaid review copy) is as about as practical as it gets. Loaded with code examples, this book leaves you in no doubt that it will help you construct your own code, against your own data in the fastest time possible. But it does not take any short-cuts.Machine Learning and Scala is a broad subject to cover and SFML does an admirable job of taking you from the first steps of data preparation, right the way through to a artificial neural networks and genetic algorithms.I work with data - it has been my day-to-day working life for over twenty years, and there is never any shortage of new territory to cover. I am predominately data engineering focused, and whilst the data content is the most important thing, it is the technology that keeps me engaged. In the final chapter of SFML Nicolas covers a nice selection of frameworks for concurrent processing. This really piqued my interest - Apache Spark is a hot topic at the moment and is given a good few pages here, reviewing its Scala and Akka heritage and demonstrating its core design principles of in-memory persistence, scheduling laziness, distributed dataset actions and shared variables. Better yet Nicolas shows you how to get Spark up and running and executing your first K-means tasks.The bulk of the book is made up with detailed reviews, explanations and implementation guides for different machine learning algorithms and methodologies. Each section is made up of a wealth of detailed explanation, detailed implementation instructions and guidelines, honestly - at times - the information density can be a little overwhelming, but it is all hugely valuable, and much appreciated.Running through the whole book is, of course, an appreciation of Scala and its abilities to perform in a distributed manner, at scale. This is the sort work that Scala, in all of its object-functional glory, was designed to address. I have read other Scala books and the programming language has always been presented in a “you may be used to, but Scala…” fashion. I am happy to say that is not the case here, with solid examples, proper real world code and thorough debriefs - Scala is presented as it should be, a language in its own right, being dealt with on its own terms.If your work or study includes components of machine learning and / or data processing - and you are looking for a modern take on some time-honoured methods, this book is work picking up and consuming, avidly.
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mathieu Dec 31, 2014
Full star icon Full star icon Full star icon Full star icon Full star icon 5
just wanted to let potential buyers know that the kindle version has none of the nuisances sometimes found in e-books. i just purchased this book (from amazon france) and did a quick check of the source code, equations, and diagrams. everything shows up perfectly. no comment on the content yet, but it looks quite interesting.
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Amazon Customer Feb 13, 2017
Full star icon Full star icon Full star icon Full star icon Full star icon 5
Very detailed. Not for beginners in scala programming
Amazon Verified review Amazon
Tomer Ben David Feb 12, 2015
Full star icon Full star icon Full star icon Full star icon Full star icon 5
My favorite book these days, using scala? doing machine learning? I don't want to read a separate book on machine learning and another one on advanced scala tailored for machine learning, can anyone please summarize scala + machine learning in the same book? Yes - This book does it. Still reading but so far so good! Perfect match for me.
Amazon Verified review Amazon
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