Class Imbalance Handling Quiz

Quiz covering Data Integrity

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

   Which of the following is the primary risk of training a machine learning model on a highly imbalanced dataset without any adjustments?

2. 2

   You are working on a fraud detection model where only 0.1% of transactions are fraudulent. Which metric is most appropriate for evaluating the model's performance?

3. 3

   Which of the following techniques is considered a form of 'undersampling'?

4. 4

   When using SMOTE (Synthetic Minority Over-sampling Technique) to address class imbalance, what is the core mechanism it uses to create new samples?

5. 5

   You decide to use 'Class Weights' in your loss function instead of resampling. What is the main advantage of this approach?