Quiz will ask 20 randomly selected questions with allotted time of . You can take the quiz more than once. Once you submit the quiz, you can review how you have done, the correct the answers for each questions and the explanation for the correct the answer.
6 Modules
Which of the following statements about accuracy in model evaluation is true?
Accuracy is the ratio of correctly predicted instances to the total instances.
Accuracy is a better metric than precision when dealing with imbalanced datasets.
Accuracy can be misleading if the dataset has a high class imbalance.
Accuracy is always a reliable metric for evaluating model performance.
What is the primary difference between precision and recall?
Precision measures the accuracy of positive predictions, while recall measures the ability to find all positive instances.
Precision is the ratio of true positives to false positives, while recall is the ratio of true positives to false negatives.
Precision is more important than recall in all scenarios.
Recall is always higher than precision in a well-performing model.
In a binary classification problem, if a model has a precision of 0.8 and a recall of 0.6, what is the F1 Score?
0.72
0.75
0.78
0.80
Which of the following metrics would be most appropriate to use when the cost of false negatives is high?
Accuracy
Precision
Recall
F1 Score
If a model has a high precision but low recall, what does this indicate about the model's performance?
The model is good at identifying positive instances but misses many.
The model is bad at identifying positive instances.
The model has a balanced performance.
The model is overfitting.
6 Modules