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Название: Fundamentals of Robust Machine Learning: Handling Outliers and Anomalies in Data ScienceАвтор: Resve Saleh, Sohaib Majzoub, A.K.Md. Ehsanes Saleh
Издательство: Wiley
Год: 2025
Страниц: 409
Язык: английский
Формат: pdf (true)
Размер: 10.1 MB
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.
