Data Science Desktop Survival Guide by Graham Williams
Desktop Survival Project Home Preface Data Science Introducing R R Constructs R Tasks R Strings R Read, Write, and Create Data Template Data Exploration Data Wrangling Data Visualisation Statistics ML Template ML Scenarios ML Activities ML Applications ML Algorithms Cluster Analysis Decision Trees Computer Vision Graph Data Privacy Literate Data Science Coding with Style Resources Bibliography Index

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Chapter: ML Template

20200607 We started with a template for data wrangling in Chapter 7, explored the data in Chapter 8, learnt how to transform the data in Chapter 9, and gained initial insights through visualisations in Chapter 10. 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 7 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.