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Springer Dirty Data Processing for Machine Learning - Springer

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Description

Data quality remains a critical challenge in both database and machine learning communities, where dirty data in training and test datasets directly undermines model accuracy and reliability. This Springer book provides a comprehensive analysis of how data quality problems impact machine learning tasks and presents effective strategies for processing dirty data. The publication explores the intersection of data cleaning methods and machine learning, addressing the persistent challenge of high cleaning costs whilst improving overall data quality. Whether you work in data science, machine learning engineering, or database management, this book examines practical approaches to identifying, understanding, and mitigating the effects of dirty data on your models and results.

Key Features

Analyses the impact of dirty data on both training and test datasets in machine learning applications.

Explores effective methods for dirty data processing to improve model accuracy and reliability.

Examines existing data cleaning techniques and their effectiveness in enhancing data quality.

Addresses the cost-benefit analysis of data cleaning in machine learning workflows.

Published by Springer, a leading academic publisher specialising in computing and AI literature.

Product Specifications

Format
paperback
Domain
Amazon UK
Release Date
01 December 2024
Listed Since
03 December 2024

Barcode

No barcode data available

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