Kyriakides / Margaritis Hands-On Ensemble Learning with Python

Build highly optimized ensemble machine learning models using scikit-learn and Keras
1. Auflage 2024
ISBN: 978-1-78961-788-7
Verlag: De Gruyter
Format: EPUB
Kopierschutz: Adobe DRM (»Systemvoraussetzungen)

E-Book, Englisch, 298 Seiten

Combine popular machine learning techniques to create ensemble models using PythonKey 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 DescriptionEnsembling 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.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 forThis 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.

Autoren/Hrsg.

Kyriakides, George

Margaritis, Konstantinos G.

Fachgebiete

• Mathematik | Informatik EDV | Informatik Informatik Mensch-Maschine-Interaktion Informationsarchitektur
• Mathematik | Informatik EDV | Informatik EDV & Informatik Allgemein
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Computer Vision
• Mathematik | Informatik EDV | Informatik Daten / Datenbanken Datenbankdesign & Datenbanktheorie
• Mathematik | Informatik EDV | Informatik Informatik Künstliche Intelligenz Mustererkennung, Biometrik

Inhaltsverzeichnis

Table of Contents - A Machine Learning Refresher
- Getting Started with Ensemble Learning
- Voting
- Stacking
- Bagging
- Boosting
- Random Forests
- Clustering
- Classifying Fraudulent Transactions
- Predicting Bitcoin Prices
- Evaluating Twitters Sentiment
- Recommending Movies with Keras
- Clustering Application: World Happiness

Über Autor(innen)

Kyriakides George :

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.Margaritis Konstantinos G. :

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.