Data Science Model Performance and Evaluation 4

Question 1

Data leakage during evaluation typically causes:

Accepted Answer

Overly optimistic performance that fails in production

Answer

Leakage lets information from the test set influence training, inflating estimates that collapse in production.

Question 2

When evaluating a model on time-series data, which validation strategy is appropriate?

Accepted Answer

Time-based (forward-chaining) splits

Answer

Time-series requires forward-chaining splits so the model never trains on future data.

Question 3

For a multi-class classification problem, macro-averaged F1 differs from micro-averaged F1 because macro:

Accepted Answer

Treats all classes equally regardless of size

Answer

Macro-averaging computes the metric per class and averages equally, giving small classes equal weight.

Question 4

Adjusting the classification threshold from 0.5 to 0.7 will most likely:

Accepted Answer

Increase precision, decrease recall

Answer

Raising the threshold makes positive predictions stricter, typically increasing precision and lowering recall.

Question 5

Which statement about adjusted R-squared is correct?

Accepted Answer

It penalizes adding predictors that don't improve the model

Answer

Adjusted R-squared penalizes extra predictors that fail to improve fit, unlike plain R-squared.

Data Science Practice Test