Book Image

Hands-On Ensemble Learning with Python

By : George Kyriakides, Konstantinos G. Margaritis
Book Image

Hands-On Ensemble Learning with Python

By: George Kyriakides, Konstantinos G. Margaritis

Overview of this book

Ensembling is a technique of combining two or more similar or dissimilar machine learning algorithms to create a model that delivers superior predictive power. This book will demonstrate how you can use a variety of weak algorithms to make a strong predictive model. With its hands-on approach, you'll not only get up to speed with the basic theory but also the application of different ensemble learning techniques. Using examples and real-world datasets, you'll be able to produce better machine learning models to solve supervised learning problems such as classification and regression. In addition to this, you'll go on to leverage ensemble learning techniques such as clustering to produce unsupervised machine learning models. As you progress, the chapters will cover different machine learning algorithms that are widely used in the practical world to make predictions and classifications. You'll even get to grips with the use of Python libraries such as scikit-learn and Keras for implementing different ensemble models. By the end of this book, you will be well-versed in ensemble learning, and have the skills you need to understand which ensemble method is required for which problem, and successfully implement them in real-world scenarios.

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Table of Contents (20 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
Section 1: Introduction and Required Software Tools
Section 1: Introduction and Required Software Tools
2
A Machine Learning Refresher
A Machine Learning Refresher
Technical requirements
Learning from data
Supervised and unsupervised learning
Performance measures
Machine learning algorithms
Summary
3
Getting Started with Ensemble Learning
Getting Started with Ensemble Learning
Technical requirements
Bias, variance, and the trade-off
Ensemble learning
Difficulties in ensemble learning
Summary
4
Section 2: Non-Generative Methods
Section 2: Non-Generative Methods
5
Voting
Voting
Technical requirements
Hard and soft voting
​Python implementation
Using scikit-learn
Summary
6
Stacking
Stacking
Technical requirements
Meta-learning
Deciding on an ensemble's composition
Python implementation
Summary
7
Section 3: Generative Methods
Section 3: Generative Methods
8
Bagging
Bagging
Technical requirements
Bootstrapping
Bagging
Python implementation
Using scikit-learn
Summary
9
Boosting
Boosting
Technical requirements
AdaBoost
Gradient boosting
Using scikit-learn
XGBoost
Summary
10
Random Forests
Random Forests
Technical requirements
Understanding random forest trees
Creating forests
Using scikit-learn
Summary
11
Section 4: Clustering
Section 4: Clustering
12
Clustering
Clustering
Technical requirements
Consensus clustering
Using OpenEnsembles
Summary
13
Section 5: Real World Applications
Section 5: Real World Applications
14
Classifying Fraudulent Transactions
Classifying Fraudulent Transactions
Technical requirements
Getting familiar with the dataset
Exploratory analysis
Voting
Stacking
Bagging
Boosting
Using random forests
Comparative analysis of ensembles
Summary
15
Predicting Bitcoin Prices
Predicting Bitcoin Prices
Technical requirements
Time series data
Voting
Stacking
Bagging
Boosting
Random forests
Summary
16
Evaluating Sentiment on Twitter
Evaluating Sentiment on Twitter
Technical requirements
Sentiment analysis tools
Getting Twitter data
Creating a model
Classifying tweets in real time
Summary
17
Recommending Movies with Keras
Recommending Movies with Keras
Technical requirements
Demystifying recommendation systems
Neural recommendation systems
Using Keras for movie recommendations
Summary
18
Clustering World Happiness
Clustering World Happiness
Technical requirements
Understanding the World Happiness Report
Creating the ensemble
Gaining insights
Summary
19
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Classifying Fraudulent Transactions

In this chapter, we will attempt to classify fraudulent transactions in a dataset concerning credit card transactions from European card holders that occurred during September 2013. The main problem in this dataset is the extremely small number of fraudulent transactions, compared to the dataset's size. These types of datasets are called unbalanced, as there are unequal percentages of each label. We will try to create ensembles that can classify our particular dataset, which contains a small number of fraudulent transactions.

In this chapter we will cover the following topics:

  • Getting familiar with the dataset
  • Exploratory analysis
  • Voting
  • Stacking
  • Bagging
  • Boosting
  • Using random forests
  • Comparative analysis of ensembles
Previous Section
End of Section 1
Next Section