Quiz 1: Understanding Definitions
What is accuracy?
a) The ratio of true positive results to all positive results
b) The ratio of true positive results to the sum of true positive and false positive results
c) The ratio of correctly predicted instances to the total instances
d) The ratio of true positive results to the sum of true positive and false negative results
Show Answer
c) The ratio of correctly predicted instances to the total instances
What is precision?
a) The ratio of true positive results to all positive results
b) The ratio of true positive results to the sum of true positive and false positive results
c) The ratio of correctly predicted instances to the total instances
d) The ratio of true positive results to the sum of true positive and false negative results
Show Answer
b) The ratio of true positive results to the sum of true positive and false positive results
What is recall?
a) The ratio of true positive results to all positive results
b) The ratio of true positive results to the sum of true positive and false positive results
c) The ratio of correctly predicted instances to the total instances
d) The ratio of true positive results to the sum of true positive and false negative results
Show Answer
d) The ratio of true positive results to the sum of true positive and false negative results
Quiz 2: Calculation Problems
Given a confusion matrix with 50 true positives (TP), 10 false positives (FP), 5 false negatives (FN), and 35 true negatives (TN), what is the accuracy?
a) 0.75
b) 0.85
c) 0.90
d) 0.95
Show Answer
a) 0.85
Accuracy = (TP + TN) / (TP + TN + FP + FN) = (50 + 35) / (50 + 35 + 10 + 5) = 85 / 100 = 0.85
Using the same confusion matrix, what is the precision?
a) 0.83
b) 0.88
c) 0.91
d) 0.95
Show Answer
b) 0.83
Precision = TP / (TP + FP) = 50 / (50 + 10) = 50 / 60 = 0.83
Using the same confusion matrix, what is the recall?
a) 0.80
b) 0.85
c) 0.90
d) 0.95
Show Answer
c) 0.91
Recall = TP / (TP + FN) = 50 / (50 + 5) = 50 / 55 = 0.91
If a model has high precision but low recall, what does it indicate?
a) The model is good at predicting the positive class but misses many positive instances
b) The model is good at predicting the negative class but misses many positive instances
c) The model predicts most instances correctly
d) The model has a high error rate
Show Answer
a) The model is good at predicting the positive class but misses many positive instances
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