In the context of BI reporting and predictive analytics, which of the following is a popular classification technique that builds a model to predict a discrete outcome from features? Select the single best answer.

Introduction / Context:
Classification assigns records to categories such as churn vs. non-churn, good vs. bad credit, or responder vs. non-responder. Many algorithms perform classification, including decision trees, naive Bayes, SVMs, and regression-based methods (notably logistic regression).

Given Data / Assumptions:

• Goal: predict a categorical/boolean outcome.
• Candidate techniques: clustering, regression, RFM.
• Terminology uses “regression” broadly to include logistic regression for classification.

Concept / Approach:

Although “regression” often implies predicting a numeric value, logistic regression is a staple for binary classification. It models the log-odds of class membership from input features and outputs class probabilities. Cluster analysis, in contrast, is unsupervised segmentation without labeled targets; RFM is a descriptive ranking approach.

Step-by-Step Solution:

Verification / Alternative check:

Industry practice: propensity models (e.g., likelihood to buy/churn) are commonly built with logistic regression due to interpretability and robustness.

Why Other Options Are Wrong:

Cluster analysis only: unsupervised; no class labels. RFM only: heuristic segmentation, not a predictive classifier. Both cluster and regression: mixing unsupervised with supervised; only regression fits classification among the options.

Common Pitfalls:

Assuming “regression” always means continuous targets; forgetting that evaluation for classification uses accuracy, AUC, precision/recall, not RMSE.

Final Answer:

regression analysis only