Once our model gets estimated as in the preceding section, it is time for us to evaluate these estimated models to see if they fit our client's criteria so that we can either move to the results explanation stage or go back to some previous stage to refine our predictive models.

From the client's perspective, there are two common error types in machine learning for churn prediction.

The first one is False Negative (Type I Error), which is about failing to identify a customer who has a high propensity to depart.

From a business perspective, this is the least desirable error as the customer is very likely to leave, and the company does not know that it lost the chance to act to keep the customers, thus adversely affecting the the company's revenue.

The second one is False Positive (Type II Error), which is about classifying a good, satisfied customer as one who is one likely to churn. 

From a business perspective, this may be acceptable as it does not impact revenue, but will create...