Tidy Modeling With R A Framework For Modeling In The Tidyverse 1st Edition Max Kuhn by Max Kuhn, Julia Silge 9781492096481, 1492096482 instant download after payment.

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.
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, e.g. 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
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 or 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.