Book Image

Machine Learning in Java - Second Edition

By : AshishSingh Bhatia, Bostjan Kaluza
Book Image

Machine Learning in Java - Second Edition

By: AshishSingh Bhatia, Bostjan Kaluza

Overview of this book

As the amount of data in the world continues to grow at an almost incomprehensible rate, being able to understand and process data is becoming a key differentiator for competitive organizations. Machine learning applications are everywhere, from self-driving cars, spam detection, document search, and trading strategies, to speech recognition. This makes machine learning well-suited to the present-day era of big data and Data Science. The main challenge is how to transform data into actionable knowledge. Machine Learning in Java will provide you with the techniques and tools you need. You will start by learning how to apply machine learning methods to a variety of common tasks including classification, prediction, forecasting, market basket analysis, and clustering. The code in this book works for JDK 8 and above, the code is tested on JDK 11. Moving on, you will discover how to detect anomalies and fraud, and ways to perform activity recognition, image recognition, and text analysis. By the end of the book, you will have explored related web resources and technologies that will help you take your learning to the next level. By applying the most effective machine learning methods to real-world problems, you will gain hands-on experience that will transform the way you think about data.

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Table of Contents (13 chapters)
Preface
Preface
Who this book is for
What this book covers
To get the most out of this book
Get in touch
Free Chapter
1
Applied Machine Learning Quick Start
Applied Machine Learning Quick Start
Machine learning and data science
Data and problem definition
Data collection
Data preprocessing
Unsupervised learning
Supervised learning
Generalization and evaluation
Summary
2
Java Libraries and Platforms for Machine Learning
Java Libraries and Platforms for Machine Learning
The need for Java
Machine learning libraries
Building a machine learning application
Summary
3
Basic Algorithms - Classification, Regression, and Clustering
Basic Algorithms - Classification, Regression, and Clustering
Before you start
Classification
Regression
Clustering
Summary
4
Customer Relationship Prediction with Ensembles
Customer Relationship Prediction with Ensembles
The customer relationship database
Basic Naive Bayes classifier baseline
Basic modeling
Advanced modeling with ensembles
Summary
5
Affinity Analysis
Affinity Analysis
Market basket analysis
Association rule learning
The supermarket dataset
Discover patterns
Other applications in various areas
Summary
6
Recommendation Engines with Apache Mahout
Recommendation Engines with Apache Mahout
Basic concepts
Getting Apache Mahout
Building a recommendation engine
Content-based filtering
Summary
7
Fraud and Anomaly Detection
Fraud and Anomaly Detection
Suspicious and anomalous behavior detection
Suspicious pattern detection
Anomalous pattern detection
Outlier detection using ELKI
Fraud detection in insurance claims
Anomaly detection in website traffic
Summary
8
Image Recognition with Deeplearning4j
Image Recognition with Deeplearning4j
Introducing image recognition
Image classification
Summary
9
Activity Recognition with Mobile Phone Sensors
Activity Recognition with Mobile Phone Sensors
Introducing activity recognition
Collecting data from a mobile phone
Building a classifier
Summary
10
Text Mining with Mallet - Topic Modeling and Spam Detection
Text Mining with Mallet - Topic Modeling and Spam Detection
Introducing text mining
Installing Mallet
Working with text data
Topic modeling for BBC News
Detecting email spam
Summary
11
What Is Next?
What Is Next?
Machine learning in real life
Standards and markup languages
Machine learning in the cloud
Web resources and competitions
Summary
12
Other Books You May Enjoy
Other Books You May Enjoy
Leave a review - let other readers know what you think

Basic Naive Bayes classifier baseline

As per the rules of the challenge, the participants had to outperform the basic Naive Bayes classifier in order to qualify for prizes, which makes an assumption that features are independent (refer to Chapter 1, Applied Machine Learning Quick Start).

The KDD Cup organizers ran the vanilla Naive Bayes classifier, without any feature selection or hyperparameter adjustments. For the large dataset, the overall scores of the Naive Bayes on the test set were as follows:

  • Churn problem: AUC = 0.6468
  • Appetency problem: AUC = 0.6453
  • Upselling problem: AUC=0.7211

Note that the baseline results are only reported for the large dataset. Moreover, while both the training and testing datasets are provided at the KDD Cup site, the actual true labels for the test set are not provided. Therefore, when we process the data with our models, there is no way to...

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