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Machine Learning for Imbalanced Data - by Kumar Abhishek & Mounir Abdelaziz (Paperback)

Machine Learning for Imbalanced Data - by  Kumar Abhishek & Mounir Abdelaziz (Paperback) - 1 of 1
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About this item

Highlights

  • 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 dataKey FeaturesUnderstand how to use modern machine learning frameworks with detailed explanations, illustrations, and code samplesLearn cutting-edge deep learning techniques to overcome data imbalanceExplore different methods for dealing with skewed data in ML and DL applicationsPurchase of the print or Kindle book includes a free eBook in the PDF formatBook DescriptionAs machine learning practitioners, we often encounter imbalanced datasets in which one class has considerably fewer instances than the other.
  • Author(s): Kumar Abhishek & Mounir Abdelaziz
  • 344 Pages
  • Computers + Internet, Intelligence (AI) & Semantics

Description



About the Book



This book will help you master the art of predicting outcomes when datasets are imbalanced and where the most important class is under-represented.



Book Synopsis



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
Dimensions (Overall): 9.25 Inches (H) x 7.5 Inches (W) x .72 Inches (D)
Weight: 1.3 Pounds
Suggested Age: 22 Years and Up
Number of Pages: 344
Genre: Computers + Internet
Sub-Genre: Intelligence (AI) & Semantics
Publisher: Packt Publishing
Format: Paperback
Author: Kumar Abhishek & Mounir Abdelaziz
Language: English
Street Date: November 30, 2023
TCIN: 91097866
UPC: 9781801070836
Item Number (DPCI): 247-33-6860
Origin: Made in the USA or Imported
If the item details above aren’t accurate or complete, we want to know about it.

Shipping details

Estimated ship dimensions: 0.72 inches length x 7.5 inches width x 9.25 inches height
Estimated ship weight: 1.3 pounds
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