Overview

Models can be used in almost any domain for purposes including prediction, inference, or simply describing data. In all these cases, the predictive capacity of a model can be used to evaluate it, and we can build better, more useful models by adhering to good statistical practice. The tidymodels framework harmonizes the heterogeneous model interfaces in R and offers a consistent, flexible framework for modeling suitable for beginners as well as the very experienced. This book provides a practical introduction to how to use R software to create models, focusing on a dialect of the R programming language called the tidyverse. Software that adopts tidyverse principles shares 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. The tidymodels framework for modeling is built to be easily understood and used by a broad range of people.

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9781492096481

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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.

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