
Tidy Modeling with R: A Framework for Modeling in the Tidyverse
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OReilly Media
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The tidymodels framework combines human-centric design with statistical best practices, and I can't think of a better way to learn it than from Max and Julia.
Hadley Wickham (Chief Scientist, RStudio)
This book offers a unified and systematic approach to building, analyzing and evaluating statistical models in R.
Balasubramanian Narasimhan (Senior Research Scientist, Stanford University)
Hadley Wickham (Chief Scientist, RStudio)
This book offers a unified and systematic approach to building, analyzing and evaluating statistical models in R.
Balasubramanian Narasimhan (Senior Research Scientist, Stanford University)
About the Author
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.
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.
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