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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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Free Chapter

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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Python implementation

The simplest way to implement hard voting in Python is to use scikit-learn to create base learners, train them on some data, and combine their predictions on test data. In order to do so, we will go through the following steps:

Load the data and split it into train and test sets
Create some base learners
Train them on the train data
Produce predictions for the test data
Combine predictions using hard voting
Compare the individual learner's predictions as well as the combined predictions with the ground truth (actual correct classes)

Although scikit-learn has implementations for voting, by creating a custom implementation, it will be easier to understand how the algorithm works. Furthermore, it will enable us to better understand how to process and analyze a base learner's outputs.

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