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

ISBN13:9781492096481
ISBN10:1492096482
Binding:Paperback
No. of pages:300 pages
Size:233x177x20 mm
Weight:658 g
Language:English
884
Category:

Tidy Modeling with R

A Framework for Modeling in the Tidyverse
 
Edition number: 1
Publisher: O?Reilly
Date of Publication:
Number of Volumes: Print PDF
 
Normal price:

Publisher's listprice:
GBP 52.99
Estimated price in HUF:
25 594 HUF (24 375 HUF + 5% VAT)
Why estimated?
 
Your price:

23 034 (21 938 HUF + 5% VAT )
discount is: 10% (approx 2 559 HUF off)
The discount is only available for 'Alert of Favourite Topics' newsletter recipients.
Click here to subscribe.
 
Availability:

Estimated delivery time: In stock at the publisher, but not at Prospero's office. Delivery time approx. 3-5 weeks.
Not in stock at Prospero.
Can't you provide more accurate information?
 
  Piece(s)

 
Long description:

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