Abstract
This chapter completes the Assemble phase and then reviews the Adopt phase. The Assemble phase makes a soft transition out of data wrangling into model developmentāillustrating the difference between finding and preparing data versus using it to build and tune a model. This chapter dives into the iterative nature of this process and shows the feedback loops that exist between the Appreciate and Assemble phases, as well as the feedback loops that exist within the Assemble phase between Data Wrangling and Model Development. Feature engineering, strata, bagging, boosting, ensembles, and different forms of validation are many of the topics reviewed. This chapter concludes with a review of how to get a model adopted. These sections tie back to Chap. 11 since strong project management will position a model to be adopted more easily once completed.
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Notes
- 1.
āPost Hocā literally translates to āafter thisā and refers to all the āanalysis after the analysis.ā This term is often used to identify digging into results and even doing more research after the initial analysis is complete.
- 2.
In this case, āmanager instabilityā means a given employee has had a large number of managers in a short period of time .
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Rosett, C.M., Hagerty, A. (2021). Bringing Your Model to Life. In: Introducing HR Analytics with Machine Learning. Springer, Cham. https://doi.org/10.1007/978-3-030-67626-1_14
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DOI: https://doi.org/10.1007/978-3-030-67626-1_14
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Online ISBN: 978-3-030-67626-1
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