Predictive Modeling Flashcards

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

Answer: Recall

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

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

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

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

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

Answer: Frequency

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

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

Answer: Random Forest

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

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

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

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

Which technique is used to reduce dimensionality by transforming features into uncorrelated principal components?

Answer: Principal Component Analysis (PCA)

PCA projects data onto orthogonal principal components that capture the most variance, reducing the number of features while preserving information.

In a customer churn prediction model, a high false negative rate means the model is:

Answer: Missing many customers who will actually churn

A high false negative rate means the model fails to predict churn for many customers who will actually leave, leading to missed retention opportunities.