- Mastering Machine Learning with R(Second Edition)
- Cory Lesmeister
- 181字
- 2021-07-09 18:23:53
Modeling
This is where all the work that you've done up to this point can lead to fist-pumping exuberance or fist-pounding exasperation. But hey, if it was that easy, everyone would be doing it. The tasks are as follows:
- Selecting a modeling technique.
- Generating a test design.
- Building a model.
- Assessing a model.
Oddly, this process step includes the considerations that you have already thought of and prepared for. In the first step, you will need at least some idea about how you will be modeling. Remember that this is a flexible, iterative process and not some strict linear flowchart such as an aircrew checklist.
The cheat sheet included in this chapter should help guide you in the right direction for the modeling techniques. Test design refers to the creation of your test and train datasets and/or the use of cross-validation and this should have been thought of and accounted for in the data preparation.
Model assessment involves comparing the models with the criteria/criterion that you developed in the business understanding, for example, RMSE, Lift, ROC, and so on.
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