14 Model Template
20200607 We started with a template for data wrangling in Chapter 8, explored the data in Chapter 9, learnt how to transform the data in Chapter 1.1, and gained initial insights through visualisations in Chapter 11. We are now ready to use machine learning to build analytic models and begin to understand the knowledge the models are capturing. In this chapter we develop a template for building and evaluating models. As with the data template in Chapter 8 the intention of the template is to provide a starting point for building and evaluating models.
R offers an full suite of model builders. We will use one such model builder in this chapter. A model builder is access via a function call and the arguments to the function are generally similar across different model builders. Non-the-less, each algorithm is generally implemented by different developers and so idiosyncratic differences occur.
Your donation will support ongoing development and give you access to the PDF version of this book. Desktop Survival Guides include Data Science, GNU/Linux, and MLHub. Books available on Amazon include Data Mining with Rattle and Essentials of Data Science. Popular open source software includes rattle, wajig, and mlhub. Hosted by Togaware, a pioneer of free and open source software since 1984.
Copyright © 1995-2021 Graham.Williams@togaware.com Creative Commons Attribution-ShareAlike 4.0.