Data Science Model Evaluation and Validation 5

Question 1

Which metric is most appropriate for evaluating a regression model?

Accepted Answer

Root Mean Squared Error (RMSE)

Answer

RMSE measures average prediction error magnitude, suited to continuous targets.

Question 2

The R-squared (coefficient of determination) value represents:

Accepted Answer

The proportion of variance in the target explained by the model

Answer

R² indicates how much of the target's variance the model accounts for.

Question 3

Compared to RMSE, Mean Absolute Error (MAE) is:

Accepted Answer

Less sensitive to large outliers

Answer

MAE weights all errors linearly, so it is less influenced by outliers than the squared RMSE.

Question 4

Adjusted R-squared is preferred over R-squared when:

Accepted Answer

Comparing models with different numbers of predictors

Answer

Adjusted R² penalizes adding predictors that don't improve the model, enabling fair comparison.

Question 5

For a multi-class classification problem, a macro-averaged F1 score:

Accepted Answer

Averages the F1 of each class equally regardless of class size

Answer

Macro-averaging treats every class equally, useful when minority classes matter.

Data Science Practice Test