Model evaluation
In the last section, we summarized what is needed to complete our model estimation for our supervised machine learning. Now it is time for us to evaluate these estimated models to see if they fit the client's criterions so that we can either move to the results explanation stage or go back to some previous stages to refine our predictive models.
To perform our model evaluation, in this section, we will need to use Root Mean Square Error (RMSE) to assess our linear regression models of predicting Call Center calls, and use confusion matrix to assess our logistic regression model of predicting customer churn, for which the following numbers are often preferred:
True Positive (TP): Label is positive and prediction is also positive
True Negative (TN): Label is negative and prediction is also negative
False Positive (FP): Label is negative but prediction is positive
False Negative (FN): Label is positive but prediction is negative
Here, positive means the subscriber departed, and...
