Abstract
The data set used in Chapters 6-9 to illustrate the model building process was based on observational data: the samples were selected from a predefined population and the predictors and response were observed. The case study in the chapter is used to explain the model building process for data that emanate from a designed experiment. In a designed experiment, the predictors and their desired values are prespecified. The specific combinations of the predictor values are also prespecified, which determine the samples that will be collected for the data set. The experiment is then conducted and the response is observed. In the context model building for a designed experiment we present a strategy (Section 10.1), recommendations for evaluating model performance (Section 10.2), an approach for identifying predictor combinations that produce an optimal response (Section 10.3), and syntax for building and evaluating models for this illustration (Section 10.4).
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Notes
- 1.
There are cases where specialized types of experimental designs are utilized with predictive models. In the field of chemometrics, an orthogonal array-type design followed by the sequential elimination of level combination algorithm has been shown to improve QSAR models (Mandal et al., 2006, 2007). Also, the field of active learning sequentially added samples based on the training set using the predictive model results (Cohn et al., 1994; Saar-Tsechansky and Provost, 2007a).
- 2.
The reader can also try simulated annealing using the code at the end of the chapter.
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Kuhn, M., Johnson, K. (2013). Case Study: Compressive Strength of Concrete Mixtures. In: Applied Predictive Modeling. Springer, New York, NY. https://doi.org/10.1007/978-1-4614-6849-3_10
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