WORKSHEET 1

CLASS - XII                     SUBJECT - AI                 CHAPTER - CAPSTONE PROJECT

1. Which concept refers to adjusting models using new data to improve their accuracy?
   • A) Refinement
   • B) Validation
   • C) Cross-validation
   • D) Feature selection
   • Answer: A
   Model Validation
2. What does the train-test split method achieve?
   • A) Collecting the data
   • B) Evaluating model performance
   • C) Data pre-processing
   • D) Model deployment
   • Answer: B
3. What percentage is commonly used for training data in a train-test split?
   • A) 80%
   • B) 20%
   • C) 67%
   • D) 50%
   • Answer: A
4. What does cross-validation help achieve?
   • A) Faster training
   • B) Model deployment
   • C) Reliable performance measures
   • D) Model transformation
   • Answer: C
5. In a cross-validation process, how many subsets are generally created in a 5-fold cross-validation?
   • A) 2
   • B) 5
   • C) 10
   • D) 3
   • Answer: B
6. When is cross-validation more beneficial than train-test split?
   • A) For large datasets
   • B) For datasets with limited rows
   • C) When doing unsupervised learning
   • D) For high computational costs
   • Answer: B
   Metrics of Model Quality
7. Which of the following is a commonly used metric for regression models?
   • A) Accuracy
   • B) Precision
   • C) Recall
   • D) Root Mean Squared Error (RMSE)
   • Answer: D
8. Which metric is most suitable for classification tasks?
   • A) MSE
   • B) Accuracy
   • C) RMSE
   • D) Noise ratio
   • Answer: B
9. What is the objective of minimizing the loss function?
   • A) Maximizing error
   • B) Improving model predictions
   • C) Increasing data complexity
   • D) Creating a noise-free dataset
   • Answer: B
10. Which of the following is used to calculate RMSE?
    • A) Mean of residuals
    • B) Sum of absolute errors
    • C) Square root of the mean of squared errors
    • D) Mean of absolute differences
    • Answer: C
11. What does a low RMSE indicate?
    • A) Poor model performance
    • B) High variance in predictions
    • C) Accurate predictions
    • D) Overfitting
    • Answer: C
    Advanced Topics and Applications