Apache Spark Machine Learning Blueprints

Model evaluation

In the previous section, we completed our model estimation. Now it is the time for us to evaluate these estimated models to check whether they fit our client's criteria so that we can either move to the explanation of results or go back to some previous stages to refine our predictive models.

To perform our model evaluation, in this section, we will use a confusion matrix and error ratio numbers. To calculate them, we need to use our test data rather than training data.

Here are the two common error types in student attrition prediction:

• False negative (Type I error): This involves failing to identify a student who has a high propensity to leave.

  From a practical perspective, this is the least desirable error as the student is very ...