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

Name: Machine Learning for Imbalanced Data - by Kumar Abhishek & Mounir Abdelaziz (Paperback)
Brand: Packt Publishing
SKU: 1013401883
Price: 51.99 USD
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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, Artificial Intelligence

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

Introduction to Data Imbalance in Machine Learning
Oversampling Methods
Undersampling Methods
Ensemble Methods
Cost-Sensitive Learning
Data Imbalance in Deep Learning
Data-Level Deep Learning Methods
Algorithm-Level Deep Learning Techniques
Hybrid Deep Learning Methods
Model Calibration
Appendix

Specifications

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

Sub-Genre: Artificial Intelligence

Publisher: Packt Publishing

Theme: General

Format: Paperback

Author: Kumar Abhishek & Mounir Abdelaziz

Language: English

Street Date: November 30, 2023

TCIN: 1013401883

UPC: 9781801070836

Item Number (DPCI): 247-62-1211

Origin: Made in the USA or Imported

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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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