Repeat the analysis for part (c) using the same cutoff threshold on model M2. Compare the F-measure results for both models. Which model is better? Are the results consistent with what you expect from the ROC curve?

You are asked to evaluate the performance of two classification models, M1

and M2. The test set you have chosen contains 26 binary attributes, labeled

as A through Z.

Table 5.5 shows the posterior probabilities obtained by applying the models to

the test set. (Only the posterior probabilities for the positive class are shown).

As this is a two-class problem, P(?)=1 ? P(+) and P(?|A, . . . , Z)=1 ?

P(+|A, . . . , Z). Assume that we are mostly interested in detecting instances

from the positive class.

When t = 0.5, the confusion matrix for M2 is shown below.



Precision = 1/2 = 50%.

Recall = 1/5 = 20%.

F-measure = (2 × .5 × .2)/(.5 + .2) = 0.2857.



Based on F-measure, M1 is still better than M2. This result is consis-