Data Science Data Science Model Performance and Evaluation Questions and Answers 3

Question 1

When comparing two models using ROC curves, what does a curve closer to the top-left corner indicate?

Accepted Answer

Better trade-off between sensitivity and specificity

Answer

A ROC curve closer to the top-left corner represents high true positive rates with low false positive rates, indicating superior discriminative ability.

Question 2

What is the main advantage of using the F-beta score with beta=2 instead of the standard F1 score?

Accepted Answer

It places more emphasis on recall than precision

Answer

F-beta with beta=2 weighs recall twice as heavily as precision, making it ideal when missing positive cases is costlier than false alarms.

Question 3

What problem does stratified sampling in cross-validation specifically address?

Accepted Answer

Preserving class distribution across folds

Answer

Stratified sampling ensures each fold maintains the same proportion of each class as the full dataset, preventing biased evaluation from uneven splits.

Question 4

Which metric would you use to evaluate a regression model's performance in terms of the same units as the target variable?

Accepted Answer

Root Mean Squared Error

Answer

RMSE takes the square root of MSE, returning the error to the original units of the target variable for more interpretable results.

Question 5

What does a learning curve showing training and validation scores converging at a low value suggest?

Accepted Answer

The model is underfitting and more data will not help

Answer

When both curves converge at a low score, the model lacks capacity to learn the underlying pattern, indicating high bias that more data cannot fix.

Data Science Practice Test

Data Science Practice Test

Data Science Data Science Model Performance and Evaluation Questions and Answers 3