Fundamentals of Robust Machine Learning

Handling Outliers and Anomalies in Data Science

Resve A. Saleh, Sohaib Majzoub, A. K. Md. Ehsanes Saleh

• Publisher: Wiley
• Year: 2025
• Print ISBN: 9781394294374
• eBook ISBN: 9781394294398
• Edition: 1
• Topic: Computer Science
• Subtopic: Computer Science General
• Index: Computer Science

An essential guide for tackling outliers and anomalies in machine learning and data science.

In recent years, machine learning (ML) has transformed virtually every area of research and technology, becoming one of the key tools for data scientists. Robust machine learning is a new approach to handling outliers in datasets, which is an often-overlooked aspect of data science. Ignoring outliers can lead to bad business decisions, wrong medical diagnoses, reaching the wrong conclusions or incorrectly assessing feature importance, just to name a few.

Fundamentals of Robust Machine Learning offers a thorough but accessible overview of this subject by focusing on how to properly handle outliers and anomalies in datasets. There are two main approaches described in the book: using outlier-tolerant ML tools, or removing outliers before using conventional tools. Balancing theoretical foundations with practical Python code, it provides all the necessary skills to enhance the accuracy, stability and reliability of ML models.

Fundamentals of Robust Machine Learning readers will also find:

• A blend of robust statistics and machine learning principles
• Detailed discussion of a wide range of robust machine learning methodologies, from robust clustering, regression and classification, to neural networks and anomaly detection
• Python code with immediate application to data science problems

Fundamentals of Robust Machine Learning is ideal for undergraduate or graduate students in data science, machine learning, and related fields, as well as for professionals in the field looking to enhance their understanding of building models in the presence of outliers.

Table of contents

• Cover
• Title Page
• Copyright
• Contents
• Preface
• About the Companion Website
• Chapter 1 Introduction
• Chapter 2 Robust Linear Regression
• Chapter 3 The Log‐Cosh Loss Function
• Chapter 4 Outlier Detection, Metrics, and Standardization
• Chapter 5 Robustness of Penalty Estimators
• Chapter 6 Robust Regularized Models
• Chapter 7 Quantile Regression Using Log‐Cosh
• Chapter 8 Robust Binary Classification
• Chapter 9 Neural Networks Using Log‐Cosh
• Chapter 10 Multi‐class Classification and Adam Optimization
• Chapter 11 Anomaly Detection and Evaluation Metrics
• Chapter 12 Case Studies in Data Science
• Index
• EULA