The Art of Feature Engineering - Essentials for Machine Learning (Paperback)

When machine learning engineers work with data sets, they may find the results aren't as good as they need. Instead of improving the model or collecting more data, they can use the feature engineering process to help improve results by modifying the data's features to better capture the nature of the problem. This practical guide to feature engineering is an essential addition to any data scientist's or machine learning engineer's toolbox, providing new ideas on how to improve the performance of a machine learning solution. Beginning with the basic concepts and techniques, the text builds up to a unique cross-domain approach that spans data on graphs, texts, time series, and images, with fully worked out case studies. Key topics include binning, out-of-fold estimation, feature selection, dimensionality reduction, and encoding variable-length data. The full source code for the case studies is available on a companion website as Python Jupyter notebooks.

General

Imprint:	Cambridge UniversityPress
Country of origin:	United Kingdom
Release date:	June 2020
Authors:	Pablo Duboue
Dimensions:	228 x 152 x 16mm (L x W x T)
Format:	Paperback - Trade
Pages:	284
ISBN-13:	978-1-108-70938-5
Categories:	Books > Computing & IT > General theory of computing > Mathematical theory of computation Books > Computing & IT > Computer programming > Software engineering Books > Computing & IT > Applications of computing > Databases > Data mining Books > Computing & IT > Applications of computing > Artificial intelligence > Machine learning
LSN:	1-108-70938-9
Barcode:	9781108709385

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