Dataset Shift in Machine Learning

by Joaquin Quiñonero-Candela (Editor), Masashi Sugiyama (Editor), Anton Schwaighofer (Editor)

An overview of recent efforts in the machine learning community to deal with dataset and covariate shift, which occurs when test and training inputs and outputs have different distributions.

Genres: Ebooks

229 pages, ebook

First published February 13, 2009

Reviews

[Xianshun Chen · Rating 2 out of 5]

The book is not for me. I was reading this book for getting ideas on how to detect and handle data shift practically. A number of chapters contains some rather unnecessary jargon which can be simplified. Also it contains quite a number of typos. The contents are theoretical but fail to relate to practical data driven examples such dataset shift detection and measurement. also chapters are loosely coupled and does not give a good sequential flow. some chapters appear to be purely "philosophical"

[Alice Holloway · Rating 5 out of 5]

This book is a solid introduction to data engineering, especially for those using Python to build and manage data pipelines. It covers essential concepts like data transformation, big data handling, and pipeline deployment with practical, real-world examples. The content is well-structured for beginners and professionals transitioning to data engineering. For those looking to complement their learning, platforms like Unidata (https://unidata.pro/) provide valuable resources, including datasets and tools that align with the book’s practical focus. Highly recommended for aspiring data engineers and IT professionals preparing for a career shift.