Hands-On Ensemble Learning with Python

Combine popular machine learning techniques to create ensemble models using Python

Key Features

• Implement ensemble models using algorithms such as random forests and AdaBoost
• Apply boosting, bagging, and stacking ensemble methods to improve the prediction accuracy of your model
• Explore real-world data sets and practical examples coded in scikit-learn and Keras

Book Description

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 on the basic theory but also the application of various 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. Furthermore, 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.

What you will learn

• Implement ensemble methods to generate models with high accuracy
• Overcome challenges such as bias and variance
• Explore machine learning algorithms to evaluate model performance
• Understand how to construct, evaluate, and apply ensemble models
• Analyze tweets in real time using Twitter's streaming API
• Use Keras to build an ensemble of neural networks for the MovieLens dataset

Who this book is for

This book is for data analysts, data scientists, machine learning engineers and other professionals who are looking to generate advanced models using ensemble techniques. An understanding of Python code and basic knowledge of statistics is required to make the most out of this book.

Table of Contents

1. A Machine Learning Refresher
2. Getting Started with Ensemble Learning
3. Voting
4. Stacking
5. Bagging
6. Boosting
7. Random Forests
8. Clustering
9. Classifying Fraudulent Transactions
10. Predicting Bitcoin Prices
11. Evaluating Twitters Sentiment
12. Recommending Movies with Keras
13. Clustering Application: World Happiness

George Kyriakides is a Ph.D. researcher, studying distributed neural architecture search. His interests and experience include automated generation and optimization of predictive models for a wide array of applications, such as image recognition, time series analysis, and financial applications. He holds an M.Sc. in computational methods and applications, and a B.Sc. in applied informatics, both from the University of Macedonia, Thessaloniki, Greece.

Konstantinos G. Margaritis has been a teacher and researcher in computer science for more than 30 years. His research interests include parallel and distributed computing as well as computational intelligence and machine learning. He holds an M.Eng. in electrical engineering (Aristotle University of Thessaloniki, Greece), as well as an M.Sc. and a Ph.D. in computer science (Loughborough University, UK). He is a professor at the Department of Applied Informatics, University of Macedonia, Thessaloniki, Greece.