MAC Predictive Modeling 3

Question 1

Which regularization technique sets some regression coefficients exactly to zero, effectively performing feature selection?

Accepted Answer

LASSO (L1)

Answer

LASSO regression uses an L1 penalty that drives some coefficients to exactly zero, automatically selecting the most relevant features.

Question 2

When building a propensity-to-buy model, the target variable is typically:

Accepted Answer

Binary — purchased or did not purchase

Answer

Propensity-to-buy models predict the probability of a binary outcome: whether a customer will purchase (1) or not (0).

Question 3

What does an AUC-ROC score of 0.5 indicate about a classification model?

Accepted Answer

The model performs no better than random chance

Answer

An AUC of 0.5 means the model's ROC curve follows the diagonal, indicating it performs no better than random guessing.

Question 4

In gradient boosting, how are subsequent models trained?

Accepted Answer

On the residual errors of previous models

Answer

Gradient boosting trains each new model to correct the residual errors (pseudo-residuals) left by the combined ensemble of previous models.

Question 5

A marketer wants to predict next month's email campaign revenue. Which type of model is most appropriate?

Accepted Answer

Regression

Answer

Predicting a continuous value like revenue requires a regression model, not a classification model.

(MAC) Marketing Analytics Certification Practice Test