Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?

A classifier has been implemented to predict whether or not someone has a specific type of disease.

Considering that only 1% of the population in the dataset has this disease, which measures will work the BEST to evaluate this model?
A . Mean squared error
B . Precision and accuracy
C . Precision and recall
D . Recall and explained variance

View Answer

Answer: C

Explanation:

Precision and recall are two measures that can evaluate the performance of a classifier, especially when the data is imbalanced. Precision is the ratio of true positives (correctly predicted positive cases) to all predicted positive cases. Recall is the ratio of true positives to all actual positive cases. Precision and recall can help assess how well the classifier can identify the positive cases (the disease) and avoid false negatives (missed diagnosis) or false positives (unnecessary treatment).