For model M1, suppose you choose the cutoff threshold to be t = 0.5. In other words, any test instances whose posterior probability is greater than t will be classified as a positive example. Compute the precision, recall, and F-measure for the model at this threshold value.

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 M1 is shown below.



Precision = 3/4 = 75%.

Recall = 3/5 = 60%.

F-measure = (2 × .75 × .6)/(.75 + .6) = 0.667.