Task 2.3: Analyze Model Performance MLA-C01 Practice Questions (Page 7)

Questions 31–35

1. 31foundation · easy

   In a ROC curve, what does the area under the curve (AUC) of 0.5 indicate about a binary classifier?

   • AThe classifier is worse than random guessing.
   • BThe classifier is perfect with no errors.
   • CThe classifier performs no better than random guessing.
   • DThe classifier always predicts the negative class.
   Reveal Solution Discussion Select an answer first
2. 32foundation · easy

   What is a sign that a model's training is diverging rather than converging?

   • ALoss decreases steadily over epochs
   • BValidation loss is lower than training loss
   • CLoss increases or oscillates wildly across epochs
   • DTraining accuracy is high
   Reveal Solution Discussion Select an answer first
4. 33foundation · easy

   What is the purpose of model interpretation in machine learning?

   • ATo understand why a model makes certain predictions
   • BTo reduce training time
   • CTo automatically retrain the model
   • DTo increase the model's accuracy
   Reveal Solution Discussion Select an answer first
5. 34foundation · easy

   For a binary classification problem with a highly imbalanced dataset where the positive class is rare, which metric is most informative to evaluate model performance?

   • AAccuracy
   • BPrecision and recall
   • CRMSE
   • DR-squared
   Reveal Solution Discussion Select an answer first
6. 35foundation · easy

   A model has high training error and high validation error. Which condition does this most likely indicate?

   • AConvergence failure
   • BData leakage
   • COverfitting
   • DUnderfitting
   Reveal Solution Discussion Select an answer first