Model Performance Metrics Quiz

5 questions Pass: 70% +25 pts (50 with Pro)

Quiz covering Model Evaluation and Selection

What you'll be asked

  1. In a binary classification task, which metric represents the proportion of actual positive cases that were correctly identified by the model?
  2. You are training a regression model on Azure Machine Learning to predict housing prices. Which metric is most appropriate if you want to penalize large errors more heavily than small ones?
  3. When evaluating a classification model where the classes are highly imbalanced (e.g., 99% negative, 1% positive), which metric is likely to be the most misleading regarding model performance?
  4. What does the Area Under the ROC Curve (AUC-ROC) score represent in a binary classification model?
  5. During hyperparameter tuning in Azure Machine Learning, you observe that your model has a very low training error but a very high validation error. Which phenomenon is this indicative of?

Answers and explanations come after you submit.