Data Science Model Performance and Evaluation 5

Question 1

Stratified k-fold cross-validation differs from standard k-fold by:

Accepted Answer

Preserving the class distribution in each fold

Answer

Stratified folds maintain the original class proportions in every fold, important for imbalanced data.

Question 2

Specificity measures a classifier's ability to correctly identify:

Accepted Answer

Actual negatives

Answer

Specificity (true negative rate) is the proportion of actual negatives correctly identified.

Question 3

If two models have identical accuracy but different ROC-AUC, the higher-AUC model is better at:

Accepted Answer

Ranking positives above negatives across thresholds

Answer

AUC reflects ranking quality across all thresholds, independent of a single decision cutoff.

Question 4

A learning curve where both training and validation error remain high and close together indicates:

Accepted Answer

Underfitting (high bias)

Answer

High, converged errors signal underfitting, where the model is too simple for the data.

Question 5

Cohen's Kappa is preferred over raw accuracy because it:

Accepted Answer

Accounts for agreement expected by chance

Answer

Cohen's Kappa corrects observed accuracy for the agreement expected by random chance.

Data Science Practice Test