Tidy Modeling With R: A Framework for Modeling in the Tidyverse - Couverture souple

Kuhn, Max; Silge, Julia

 
9781492096481: Tidy Modeling With R: A Framework for Modeling in the Tidyverse
Couverture souple
ISBN 10 : 1492096482 ISBN 13 : 9781492096481
Editeur : O'Reilly Media, 2022

Synopsis

Get going with tidymodels, a collection of R packages for modeling and machine learning. Whether you're just starting out or have years of experience with modeling, this practical introduction shows data analysts, business analysts, and data scientists how the tidymodels framework offers a consistent, flexible approach for your work.

RStudio engineers Max Kuhn and Julia Silge demonstrate ways to create models by focusing on an R dialect called the tidyverse. Software that adopts tidyverse principles shares both a high-level design philosophy and low-level grammar and data structures, so learning one piece of the ecosystem makes it easier to learn the next. You'll understand why the tidymodels framework has been built to be used by a broad range of people.

With this book, you will:

  • Learn the steps necessary to build a model from beginning to end
  • Understand how to use different modeling and feature engineering approaches fluently
  • Examine the options for avoiding common pitfalls of modeling, such as overfitting
  • Learn practical methods to prepare your data for modeling
  • Tune models for optimal performance
  • Use good statistical practices to compare, evaluate, and choose among models

Les informations fournies dans la section « Synopsis » peuvent faire référence à une autre édition de ce titre.

À propos des auteurs

Max Kuhn is a software engineer at RStudio. He is currently working on improving R's modeling capabilities. He was applying models in the pharmaceutical and diagnostic industries for over 18 years. Max has a Ph.D. in Biostatistics and is the author of numerous R packages for techniques in machine learning. He, and Kjell Johnson, wrote the book "Applied Predictive Modeling", which won the Ziegel award from the American Statistical Association, which recognizes the best book reviewed in Technometrics in 2015. Their second book, "Feature Engineering and Selection", was published in 2019.

Julia Silge is a software engineer at RStudio PBC where she works on open source modeling tools. She holds a PhD in astrophysics and has worked as a data scientist in tech and the nonprofit sector, as well as a technical advisory committee member for the US Bureau of Labor Statistics. She is an author of multiple books, an international keynote speaker, and a real-world practitioner focusing on data analysis and machine learning practice. Julia loves text analysis, making beautiful charts, and communicating about technical topics with diverse audiences.

Les informations fournies dans la section « A propos du livre » peuvent faire référence à une autre édition de ce titre.

Éditeur
O'Reilly Media
Date d'édition
2022
Langue
anglais
ISBN 10 
1492096482
ISBN 13 
9781492096481
Reliure
Broché
Nombre de pages
381
Coordonnées du fabricant
O'Reilly
https://oreilly.com

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