Question

A university is applying classification methods in order to identify alumni who may be interested in donating money. The university has a database of 58,205 alumni profiles containing numerous variables. Of these 58,205 alumni, only 576 have donated in the past. The university has oversampled the data and trained a random forest of 100 classification trees. For a cutoff value of 0.5, the following confusion matrix summarizes the performance of the random forest on a validation set:
Predicted
Actual No Donation Donation
Donation 268 20
No Donation 5375 23439
The following table lists some information on individual observations from the validation set:
0.8
Observation ID Actual Class Probability of Donation Predicted Class
A Donation 0.8 No Donation
B No Donation 0.1 Donation
C No Donation 0.6 Donation
Compute the values of accuracy, sensitivity, specificity, and precision.

Answer 1

Answer:

Accuracy = 0.81

Sensitivity = 0.93

Specificity = 0.81

Precision = 0.047

Step-by-step explanation:

Given the confusion matrix :

Actual_______ Donation___ No Donation

Donation______ 268 (TP) _______ 20 (FN)

No Donation ___5375 (FP) _____23439 (TN)

Accuracy is calculated as :

(TP + TN) / (TP+TN+FP+FN)

(268 + 23439) / (268 + 23439 + 5375 + 20)

ACCURACY = (23707 / 29102) = 0.81

Sensitivity (True positive rate) :

TP ÷ (TP + FN)

268 ÷ (268 + 20)

268 ÷ 288 = 0.93

Specificity (True Negative rate) :

TN ÷ (TN + FP)

23439 ÷ (23439 + 5375)

23439 ÷ 28814

= 0.81

Precision :

TP ÷ (TP + FP)

268 ÷ (268 + 5375)

268 ÷ 5643

= 0.047