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Mastering Java Machine Learning
Mastering Java Machine Learning

Mastering Java Machine Learning: A Java developer's guide to implementing machine learning and big data architectures

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Profile Icon Kamath Profile Icon Krishna Choppella
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (9 Ratings)
Paperback Jul 2017 556 pages 1st Edition
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Profile Icon Kamath Profile Icon Krishna Choppella
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Full star icon Full star icon Full star icon Half star icon Empty star icon 3.4 (9 Ratings)
Paperback Jul 2017 556 pages 1st Edition
eBook
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Mastering Java Machine Learning

Chapter 2. Practical Approach to Real-World Supervised Learning

The ability to learn from observations accompanied by marked targets or labels, usually in order to make predictions about unseen data, is known as supervised machine learning. If the targets are categories, the problem is one of classification and if they are numeric values, it is called regression. In effect, what is being attempted is to infer the function that maps the data to the target. Supervised machine learning is used extensively in a wide variety of machine learning applications, whenever labeled data is available or the labels can be added manually.

The core assumption of supervised machine learning is that the patterns that are learned from the data used in training will manifest themselves in yet unseen data.

In this chapter, we will discuss the steps used to explore, analyze, and pre-process the data before proceeding to training models. We will then introduce different modeling techniques ranging from...

Formal description and notation

We would like to introduce some notation and formal definitions for the terms used in supervised learning. We will follow this notation through the rest of the book when not specified and extend it as appropriate when new concepts are encountered. The notation will provide a precise and consistent language to describe the terms of art and enable a more rapid and efficient comprehension of the subject.

  • Instance: Every observation is a data instance. Normally the variable X is used to represent the input space. Each data instance has many variables (also called features) and is referred to as x (vector representation with bold) of dimension d where d denotes the number of variables or features or attributes in each instance. The features are represented as x = (x1,x2,…xd)T, where each value is a scalar when it is numeric corresponding to the feature value.
  • Label: The label (also called target) is the dependent variable of interest, generally denoted by...

Data transformation and preprocessing

In this section, we will cover the broad topic of data transformation. The main idea of data transformation is to take the input data and transform it in careful ways so as to clean it, extract the most relevant information from it, and to turn it into a usable form for further analysis and learning. During these transformations, we must only use methods that are designed while keeping in mind not to add any bias or artifacts that would affect the integrity of the data.

Feature construction

In the case of some datasets, we need to create more features from features we are already given. Typically, some form of aggregation is done using common aggregators such as average, sum, minimum, or maximum to create additional features. In financial fraud detection, for example, Card Fraud datasets usually contain transactional behaviors of accounts over various time periods during which the accounts were active. Performing behavioral synthesis such as by capturing...

Feature relevance analysis and dimensionality reduction

The goal of feature relevance and selection is to find the features that are discriminating with respect to the target variable and help reduce the dimensions of the data [1,2,3]. This improves the model performance mainly by ameliorating the effects of the curse of dimensionality and by removing noise due to irrelevant features. By carefully evaluating models on the validation set with and without features removed, we can see the impact of feature relevance. Since the exhaustive search for k features involves 2k – 1 sets (consider all combinations of k features where each feature is either retained or removed, disregarding the degenerate case where none is present) the corresponding number of models that have to be evaluated can become prohibitive, so some form of heuristic search techniques are needed. The most common of these techniques are described next.

Feature search techniques

Some of the very common search techniques...

Model building

In real-world problems, there are many constraints on learning and many ways to assess model performance on unseen data. Each modeling algorithm has its strengths and weaknesses when applied to a given problem or to a class of problems in a particular domain. This is articulated in the famous No Free Lunch Theorem (NFLT), which says—for the case of supervised learning—that averaged over all distributions of data, every classification algorithm performs about as well as any other, including one that always picks the same class! Application of NFLT to supervised learning and search and optimization can be found at http://www.no-free-lunch.org/.

In this section, we will discuss the most commonly used practical algorithms, giving the necessary details to answer questions such as what are the algorithm's inputs and outputs? How does it work? What are the advantages and limitations to consider while choosing the algorithm? For each model, we will include sample...

Model assessment, evaluation, and comparisons

The key ideas discussed here are:

  • How to assess or estimate the performance of the classifier on unseen datasets that it will be predicting on future unseen datasets.
  • What are the metrics that we should use to assess the performance of the model?
  • How do we compare algorithms if we have to choose between them?

Model assessment

In order to train the model(s), tune the model parameters, select the models, and finally estimate the predictive behavior of models on unseen data, we need many datasets. We cannot train the model on one set of data and estimate its behavior on the same set of data, as it will have a clear optimistic bias and estimations will be unlikely to match the behavior in the unseen data. So at a minimum, there is a need to partition data available into training sets and testing sets. Also, we need to tune the parameters of the model and test the effect of the tuning on a separate dataset before we perform testing on the test set. The...

Formal description and notation


We would like to introduce some notation and formal definitions for the terms used in supervised learning. We will follow this notation through the rest of the book when not specified and extend it as appropriate when new concepts are encountered. The notation will provide a precise and consistent language to describe the terms of art and enable a more rapid and efficient comprehension of the subject.

  • Instance: Every observation is a data instance. Normally the variable X is used to represent the input space. Each data instance has many variables (also called features) and is referred to as x (vector representation with bold) of dimension d where d denotes the number of variables or features or attributes in each instance. The features are represented as x = (x1,x2,…xd)T, where each value is a scalar when it is numeric corresponding to the feature value.

  • Label: The label (also called target) is the dependent variable of interest, generally denoted by y. In...

Data transformation and preprocessing


In this section, we will cover the broad topic of data transformation. The main idea of data transformation is to take the input data and transform it in careful ways so as to clean it, extract the most relevant information from it, and to turn it into a usable form for further analysis and learning. During these transformations, we must only use methods that are designed while keeping in mind not to add any bias or artifacts that would affect the integrity of the data.

Feature construction

In the case of some datasets, we need to create more features from features we are already given. Typically, some form of aggregation is done using common aggregators such as average, sum, minimum, or maximum to create additional features. In financial fraud detection, for example, Card Fraud datasets usually contain transactional behaviors of accounts over various time periods during which the accounts were active. Performing behavioral synthesis such as by capturing...

Feature relevance analysis and dimensionality reduction


The goal of feature relevance and selection is to find the features that are discriminating with respect to the target variable and help reduce the dimensions of the data [1,2,3]. This improves the model performance mainly by ameliorating the effects of the curse of dimensionality and by removing noise due to irrelevant features. By carefully evaluating models on the validation set with and without features removed, we can see the impact of feature relevance. Since the exhaustive search for k features involves 2k – 1 sets (consider all combinations of k features where each feature is either retained or removed, disregarding the degenerate case where none is present) the corresponding number of models that have to be evaluated can become prohibitive, so some form of heuristic search techniques are needed. The most common of these techniques are described next.

Feature search techniques

Some of the very common search techniques employed...

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Key benefits

  • Comprehensive coverage of key topics in machine learning with an emphasis on both the theoretical and practical aspects
  • More than 15 open source Java tools in a wide range of techniques, with code and practical usage.
  • More than 10 real-world case studies in machine learning highlighting techniques ranging from data ingestion up to analyzing the results of experiments, all preparing the user for the practical, real-world use of tools and data analysis.

Description

Java is one of the main languages used by practicing data scientists; much of the Hadoop ecosystem is Java-based, and it is certainly the language that most production systems in Data Science are written in. If you know Java, Mastering Machine Learning with Java is your next step on the path to becoming an advanced practitioner in Data Science. This book aims to introduce you to an array of advanced techniques in machine learning, including classification, clustering, anomaly detection, stream learning, active learning, semi-supervised learning, probabilistic graph modeling, text mining, deep learning, and big data batch and stream machine learning. Accompanying each chapter are illustrative examples and real-world case studies that show how to apply the newly learned techniques using sound methodologies and the best Java-based tools available today. On completing this book, you will have an understanding of the tools and techniques for building powerful machine learning models to solve data science problems in just about any domain.

Who is this book for?

This book will appeal to anyone with a serious interest in topics in Data Science or those already working in related areas: ideally, intermediate-level data analysts and data scientists with experience in Java. Preferably, you will have experience with the fundamentals of machine learning and now have a desire to explore the area further, are up to grappling with the mathematical complexities of its algorithms, and you wish to learn the complete ins and outs of practical machine learning.

What you will learn

  • Master key Java machine learning libraries, and what kind of problem each can solve, with theory and practical guidance.
  • Explore powerful techniques in each major category of machine learning such as classification, clustering, anomaly detection, graph modeling, and text mining.
  • Apply machine learning to real-world data with methodologies, processes, applications, and analysis.
  • Techniques and experiments developed around the latest specializations in machine learning, such as deep learning, stream data mining, and active and semi-supervised learning.
  • Build high-performing, real-time, adaptive predictive models for batch- and stream-based big data learning using the latest tools and methodologies.
  • Get a deeper understanding of technologies leading towards a more powerful AI applicable in various domains such as Security, Financial Crime, Internet of Things, social networking, and so on.

Product Details

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Publication date : Jul 11, 2017
Length: 556 pages
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Language : English
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Length: 556 pages
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ISBN-13 : 9781785880513
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Table of Contents

12 Chapters
1. Machine Learning Review Chevron down icon Chevron up icon
2. Practical Approach to Real-World Supervised Learning Chevron down icon Chevron up icon
3. Unsupervised Machine Learning Techniques Chevron down icon Chevron up icon
4. Semi-Supervised and Active Learning Chevron down icon Chevron up icon
5. Real-Time Stream Machine Learning Chevron down icon Chevron up icon
6. Probabilistic Graph Modeling Chevron down icon Chevron up icon
7. Deep Learning Chevron down icon Chevron up icon
8. Text Mining and Natural Language Processing Chevron down icon Chevron up icon
9. Big Data Machine Learning – The Final Frontier Chevron down icon Chevron up icon
A. Linear Algebra Chevron down icon Chevron up icon
B. Probability Chevron down icon Chevron up icon
Index Chevron down icon Chevron up icon

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1 star 22.2%
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Satheesh Oct 04, 2018
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Excellent explanation and neat way of organizing
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Marcelo Zambrana Feb 15, 2018
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Great to get started wit ML even though it says Java ML the math, concepts, examples could be applied to other languages.Highly recommend!
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DKJ Oct 03, 2017
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This is a great book that will definitely help you understand various topics under the machine learning umbrella. I enjoy studying data science however I was having a little bit of difficulty understanding Bayesian networks. After spending some time reading the content around this topic I was able to obtain a clear understanding of how Bayesian networks are developed and how to use them in practice. Overall - good book in the data science arena to have in your library.
Amazon Verified review Amazon
yashevde Jul 28, 2017
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This is a fantastic book for all levels of knowledge and ability with Java and Machine Lewarning. It has been a tremendous resource for my study of the subject
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Nishikant D. Sep 17, 2017
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Excellent book and thoroughly written for beginner and advanced study.
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