AI Model Evaluation Quiz

In a medical test for a rare disease, out of 1000 people tested, 50 actually have the disease while 950 do not. The test correctly identifies 40 out of the 50 people with the disease as positive, but it also wrongly identifies 30 of the healthy individuals as positive. What is the accuracy of the test?

• 97%

• 90%

• 85%

• 70%

A student solved 90 out of 100 questions correctly in a multiple-choice exam. What is the error rate of the student's answers?

• 10%

• 9%

• 8%

In a spam email detection system, out of 1000 emails received, 300 are spam. The system correctly identifies 240 spam emails as spam, but it also marks 60 legitimate emails as spam. What is the precision of the system?

• 80%

• 70%

• 75%

• 90%

In a binary classification problem, a model predicts 70 instances as positive out of which 50 are actually positive. What is the recall of the model?

• 50%

• 70%

• 80%

• 100%

In a sentiment analysis task, a model correctly predicts 120 positive sentiments out of 200 positive instances. However, it also incorrectly predicts 40 negative sentiments as positive. What is the F1 score of the model?

• 0.8

• 0.75

• 0.72

• 0.82

A medical diagnostic test is designed to detect a certain disease. Out of 1000 people tested, 100 have the disease, and the test identifies 90 of them correctly. However, it also wrongly identifies 50 healthy people as having the disease. What is the precision of the test?

• 90%

• 80%

• 70%

• 60%

A teacher's marks prediction system predicts the marks of a student as 75, but the actual marks obtained by the student are 80. What is the absolute error in the prediction?

• 5

• 10

• 15

• 20

The goal when evaluating an AI model is to:

• Maximize error and minimize accuracy

• Minimize error and maximize accuracy

• Focus solely on the number of data points used

• Prioritize the complexity of the model

A high F1 score generally suggests:

• A significant imbalance between precision and recall

• A good balance between precision and recall

• A model that only performs well on specific data points

• The need for more training data

How is the relationship between model performance and accuracy described?

• Inversely proportional

• Not related

• Directly proportional

• Randomly fluctuating