Hands-On Ensemble Learning with Python

By : Kyriakides, Margaritis

Buy this Book

Hands-On Ensemble Learning with Python

By: Kyriakides, Margaritis

Buy this Book

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.

Preface

Who this book is for

What this book covers

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Section 1: Introduction and Required Software Tools

A Machine Learning Refresher

Technical requirements

Learning from data

Supervised and unsupervised learning

Performance measures

Machine learning algorithms

Summary

Getting Started with Ensemble Learning

Technical requirements

Bias, variance, and the trade-off

Ensemble learning

Difficulties in ensemble learning

Summary

Section 2: Non-Generative Methods

Voting

Technical requirements

Hard and soft voting

Python implementation

Using scikit-learn

Summary

Stacking

Technical requirements

Meta-learning

Deciding on an ensemble's composition

Python implementation

Summary

Section 3: Generative Methods

Bagging

Technical requirements

Bootstrapping

Bagging

Python implementation

Using scikit-learn

Summary

Boosting

Technical requirements

AdaBoost

Gradient boosting

Using scikit-learn

XGBoost

Summary

Random Forests

Technical requirements

Understanding random forest trees

Creating forests

Using scikit-learn

Summary

Section 4: Clustering

Clustering

Technical requirements

Consensus clustering

Using OpenEnsembles

Summary

Section 5: Real World Applications

Classifying Fraudulent Transactions

Technical requirements

Getting familiar with the dataset

Exploratory analysis

Voting

Stacking

Bagging

Boosting

Using random forests

Comparative analysis of ensembles

Summary

Predicting Bitcoin Prices

Technical requirements

Time series data

Voting

Stacking

Bagging

Boosting

Random forests

Summary

Evaluating Sentiment on Twitter

Technical requirements

Sentiment analysis tools

Getting Twitter data

Creating a model

Classifying tweets in real time

Summary

Recommending Movies with Keras

Technical requirements

Demystifying recommendation systems

Neural recommendation systems

Using Keras for movie recommendations

Summary

Clustering World Happiness

Technical requirements

Understanding the World Happiness Report

Creating the ensemble

Gaining insights

Summary

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Summary

In this chapter, we presented the K-means clustering algorithm and clustering ensemble methods. We explained how majority voting can be used in order to combine cluster assignments from an ensemble, and how it can outperform the individual base learners. Furthermore, we presented the OpenEnsembles Python library, which is dedicated to clustering ensembles. The chapter can be summarized as follows.

K-means creates K clusters, and assigns instances to each cluster by iteratively considering the cluster center to be the mean of its members. It can be sensitive to the initial conditions, and the selected number of clusters. Majority voting can help to overcome the algorithm's disadvantages. Majority voting clusters together instances that have a high co-occurrence. Co-occurrence matrices show how frequently a pair of instances has been assigned to the same cluster by...