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

Higher false positive rate

Answer

Lower overall accuracy

Answer

More overfitting to training data

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

It equally weights precision and recall

Answer

It places more emphasis on precision than recall

Answer

It eliminates the need for a classification threshold

Question 3

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

Accepted Answer

Preserving class distribution across folds

Answer

Temporal data leakage

Answer

Reducing computation time

Answer

Eliminating multicollinearity

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

Mean Squared Error

Answer

R-squared

Answer

Mean Absolute Percentage Error

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

The model is overfitting

Answer

The model needs regularization

Answer

The model has reached optimal performance

Question 6

In a confusion matrix for a binary classifier, what does the false negative rate (FNR) represent?

Accepted Answer

The proportion of actual positives that were incorrectly classified as negative

Answer

The proportion of negative predictions that are incorrect

Answer

The proportion of all predictions that are negative

Answer

The proportion of actual negatives that were correctly identified