Mastering Machine Learning with R
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Evaluation

With the evaluation process, the main goal is to confirm that the work that has been done and the model selected at this point meets the business objective. Ask yourself and others, have we achieved the definition of success? Let the Netflix prize serve as a cautionary tale here. I'm sure you are aware that Netflix awarded a $1 million prize to the team that could produce the best recommendation algorithm as defined by the lowest RMSE. However, Netflix did not implement it because the incremental accuracy gained was not worth the engineering effort! Always apply Occam's razor. At any rate, here are the tasks:

  1. Evaluate the results
  2. Review the process
  3. Determine the next steps

In reviewing the process, it may be necessary—as you no doubt determined earlier in the process—to take the results through governance and communicate with the other stakeholders in order to gain their buy-in. As for the next steps, if you want to be a change agent, make sure that you answer the what, so what, and now what in the stakeholders' minds. If you can tie their now what into the decision that you made earlier, you are money.