Evaluating Model Performance: AUC and Lift

In this exercise, we will evaluate the performance of the classification model we built in the previous exercise. We will use two performance measures: the Area Under the Curve (AUC) and the Lift score.

First, the AUC.

AUC::auc(roc(predLRlasso,yTEST))

[1] 0.7753135

Second, the lift score.

lift::TopDecileLift(predLRlasso, yTEST)

[1] 3.424

Multiple choice

Type the number of the correct answer into the Dodona environment.

  1. A high value for the AUC and a low value for the lift score is preferred.
  2. A high value for the AUC as well as for the lift score is preferred.
  3. A low value for the AUC as well as for the lift score is preferred.
  4. A low value for the AUC and a high value for the lift score is preferred.

Multiple choice

Type the number of the correct answer into the Dodona environment.

  1. The AUC can be defined as the probability that a randomly selected case will have a lower test result than a randomly selected control.
  2. A lift value greater than 1 indicates that the rule body and the rule head appear less often together than expected, this means that the occurrence of the rule body has a negative effect on the occurrence of the rule head.
  3. A lift value greater than 1 indicates that the rule body and the rule head appear more often together than expected, this means that the occurrence of the rule body has a positive effect on the occurrence of the rule head.
  4. The lower the AUC, the better the performance of the model at distinguishing between the positive and negative classes.