Chapter 15. Screening Many Models

We introduced workflow sets in Chapter 7 and demonstrated how to use them with resampled data sets in Chapter 11. In this chapter, we discuss these sets of multiple modeling workflows in more detail and describe a use case where they can be helpful.

For projects with new data sets that have not yet been well understood, a data practitioner may need to screen many combinations of models and preprocessors. It is common to have little or no a priori knowledge about which method will work best with a novel data set.


A good strategy is to spend some initial effort trying a variety of modeling approaches, determine what works best, then invest additional time tweaking/optimizing a small set of models.

Workflow sets provide a user interface to create and manage this process. We’ll also demonstrate how to evaluate these models efficiently using the racing methods discussed later in this chapter.

Modeling Concrete Mixture Strength

To demonstrate how to screen multiple model workflows, we will use the concrete mixture data from Applied Predictive Modeling (Kuhn and Johnson 2013) as an example. Chapter 10 of that book demonstrated models to predict the compressive strength of concrete mixtures using the ingredients as predictors. A wide variety of models were evaluated with different predictor sets and preprocessing needs. How can workflow sets make such a process of large-scale testing for models easier?

First, let’s define the data splitting and ...

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