MAC Predictive Modeling 2

Question 1

Which metric measures the proportion of actual positives correctly identified by a classification model?

Accepted Answer

Recall

Answer

Recall (also called sensitivity or true positive rate) measures the proportion of actual positives that the model correctly identifies.

Question 2

In the context of predictive modeling, what does 'overfitting' mean?

Accepted Answer

The model learns noise in the training data and performs poorly on new data

Answer

Overfitting occurs when a model memorizes training data noise, resulting in high training accuracy but poor generalization to unseen data.

Question 3

A marketing team uses RFM analysis. What does 'F' stand for?

Accepted Answer

Frequency

Answer

In RFM analysis, F stands for Frequency — how often a customer makes a purchase within a given time period.

Question 4

Which ensemble method trains multiple decision trees on random subsets of data and averages their predictions?

Accepted Answer

Random Forest

Answer

Random Forest builds many decision trees on bootstrapped data samples with random feature subsets and averages predictions to reduce variance.

Question 5

What is the purpose of a confusion matrix in evaluating a predictive model?

Accepted Answer

To show true positives, false positives, true negatives, and false negatives

Answer

A confusion matrix summarizes classification results by showing counts of true positives, false positives, true negatives, and false negatives.

(MAC) Marketing Analytics Certification Practice Test