Evaluation Metrics Quiz

Quiz covering Model Analysis

Questions in this quiz

A preview of the 5 questions covered. Start the quiz above to answer them, check your score, and read the explanations.

1. 1

   In a binary classification task, which metric is most appropriate if the dataset is highly imbalanced and the cost of missing a positive case is extremely high?

2. 2

   Which of the following best describes the purpose of the F1-Score?

3. 3

   When evaluating a regression model, why might you prefer Mean Absolute Error (MAE) over Mean Squared Error (MSE)?

4. 4

   What does a high Area Under the Receiver Operating Characteristic Curve (AUC-ROC) value indicate about a binary classifier?

5. 5

   You are training a model where the goal is to predict the exact price of a house. You notice your model has a very low training error but a very high validation error. Which metric would best help you confirm this overfitting behavior?