Tidy Modeling with R: A Framework for Modeling in the Tidyverse

by Max Kuhn, Julia Silge

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

Genres: Programming, Nonfiction

Unknown Binding

First published January 1, 2020

Reviews

[Lord Farquaard · Rating 4 out of 5]

This book covers the fundamentals of machine learning in R. Standalone, it is sufficient to teach a large chunk of data science. Methods for enhancing computational times in both parallel processing and racing, resampling, the different types of parameter tuning, pre-processing (standardising, dummy variables, PCA, etc), model ensembles, inference and much more were implemented with seamless functions / conventions.

However, this was years ago, in my opinion R has been slowly eclipsed by Python in all aspects related to machine learning. Personally, I do not use tidymodels anymore.

[Wej · Rating 5 out of 5]

Brief introduction to the family of tidymodels packages. The early version of this book is available online. The authors focused on describing the usage of the tidyverse/tidymodels packages to fit and assess models using caret-like syntax enriched by recipes in parsnip and metrics in yardstick. Very good material to get acquainted with RStudio's machine learning packages.

[Emil O. W. Kirkegaard · Rating 5 out of 5]

Great!

[sbs transit · Rating 5 out of 5]

#nsreads 49

I love their cover illustrations honestly