Machine Learning for Imbalanced Data

Kumar Abhishek, Dr. Mounir Abdelaziz

ISBN 10: 1801070830 / ISBN 13: 9781801070836

Verlag: Packt Publishing Limited, GB, 2023

Beschreibung

This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented. You'll learn the skills you need to overcome challenges when dealing with imbalanced datasets for both traditional machine learning and deep learning models.

Bestandsnummer des Verkäufers LU-9781801070836

Bibliografische Details

• Titel: Machine Learning for Imbalanced Data
• Verlag: Packt Publishing Limited, GB
• Erscheinungsjahr: 2023
• Sprache: Englisch
• ISBN-10: 1801070830
• ISBN-13: 9781801070836
• Einband: Paperback
• Zustand: New
• Abmessungen: 19,05B x 1,98 H x 23,5 T Zentimeter Breite: 19,05 cm, Höhe: 1,98 cm, Tiefe: 23,5 cm

Über diesen Titel

Inhaltsangabe

Take your machine learning expertise to the next level with this essential guide, utilizing libraries like imbalanced-learn, PyTorch, scikit-learn, pandas, and NumPy to maximize model performance and tackle imbalanced data

Key Features

• Understand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samples
• Learn cutting-edge deep learning techniques to overcome data imbalance
• Explore different methods for dealing with skewed data in ML and DL applications
• Purchase of the print or Kindle book includes a free eBook in the PDF format

Book Description

As machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other. Many machine learning algorithms assume an equilibrium between majority and minority classes, leading to suboptimal performance on imbalanced data. This comprehensive guide helps you address this class imbalance to significantly improve model performance.

Machine Learning for Imbalanced Data begins by introducing you to the challenges posed by imbalanced datasets and the importance of addressing these issues. It then guides you through techniques that enhance the performance of classical machine learning models when using imbalanced data, including various sampling and cost-sensitive learning methods.

As you progress, you’ll delve into similar and more advanced techniques for deep learning models, employing PyTorch as the primary framework. Throughout the book, hands-on examples will provide working and reproducible code that’ll demonstrate the practical implementation of each technique.

By the end of this book, you’ll be adept at identifying and addressing class imbalances and confidently applying various techniques, including sampling, cost-sensitive techniques, and threshold adjustment, while using traditional machine learning or deep learning models.

What you will learn

• Use imbalanced data in your machine learning models effectively
• Explore the metrics used when classes are imbalanced
• Understand how and when to apply various sampling methods such as over-sampling and under-sampling
• Apply data-based, algorithm-based, and hybrid approaches to deal with class imbalance
• Combine and choose from various options for data balancing while avoiding common pitfalls
• Understand the concepts of model calibration and threshold adjustment in the context of dealing with imbalanced datasets

Who this book is for

This book is for machine learning practitioners who want to effectively address the challenges of imbalanced datasets in their projects. Data scientists, machine learning engineers/scientists, research scientists/engineers, and data scientists/engineers will find this book helpful. Though complete beginners are welcome to read this book, some familiarity with core machine learning concepts will help readers maximize the benefits and insights gained from this comprehensive resource.

Table of Contents

1. Introduction to Data Imbalance in Machine Learning
2. Oversampling Methods
3. Undersampling Methods
4. Ensemble Methods
5. Cost-Sensitive Learning
6. Data Imbalance in Deep Learning
7. Data-Level Deep Learning Methods
8. Algorithm-Level Deep Learning Techniques
9. Hybrid Deep Learning Methods
10. Model Calibration
11. Appendix

Über die Autorin bzw. den Autor

Born in patna, raised in orissa, Kumar Abhishek writes under the pen name "dr.ofletters", a poet by profession. The author is a law graduate from Chanakya National Law University, Patna, India. The author started to write prose and poems at a very young age. While still in law school, published an ebook. The author writes on topics such as mental health, quotes, philosophy, little details that describe everyday life. A zen g-d