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CRC Press Feature Engineering and Selection Data Science Book

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Description

Improve your predictive modeling workflow with this practical guide from the Chapman and Hall/CRC Data Science Series. While many resources focus solely on modeling algorithms, this book addresses the essential stages of finding the best representations of predictors to enhance model performance. Readers will learn effective techniques for selecting the best subset of predictors, a step often neglected in standard machine learning tutorials. By mastering these methods, you can build more accurate and efficient models. The text uses a variety of example data sets to demonstrate these concepts in real-world scenarios. To ensure you can apply these lessons immediately, the book includes R programs for reproducing all results. This makes it an excellent resource for data scientists, researchers, and students looking to bridge the gap between theoretical algorithms and practical, high-performing predictive models.

Key Features

Learn techniques for finding the best representations of predictors to improve your modeling results.

Master subset selection methods to identify the most effective predictors for your specific models.

Gain practical experience through the use of various example data sets throughout the text.

Reproduce all results easily using the included R programs provided in the book.

Fill the knowledge gap between selecting algorithms and the essential stages of the modeling process.

Product Specifications

Format
hardcover
Domain
Amazon UK
Release Date
02 August 2019
Listed Since
01 May 2019

Barcode

No barcode data available

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