£108.01

Springer - Dirty Data Processing for Machine Learning Book

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Description

Data quality is a major challenge in the database and machine learning communities. When training and test datasets contain quality issues, often referred to as "dirty data," the accuracy of your machine learning results can be negatively affected. This book from Springer addresses these challenges head-on by analyzing how dirty data impacts various tasks. Readers will explore effective methods for dirty data processing to improve model performance. While traditional cleaning methods can improve data quality, they often come with high costs. This text examines these impacts and looks at ways to manage the relationship between data quality and processing efficiency. It is a valuable resource for anyone working in data mining or machine learning who needs to understand and mitigate the risks posed by imperfect datasets.

Key Features

Analyzes the specific impacts that dirty data has on machine learning and data mining tasks.

Explores effective methods for processing dirty data to improve the accuracy of results.

Examines the relationship between data quality in training and test datasets.

Addresses the high costs associated with existing data cleaning methods.

Provides insights into how data quality issues affect machine learning accuracy.

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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