You want to create a model to figure out if a customer would be likely to repurchase a certain item. The project owner doesn't want you to create anything too complicated, and you have a limited data set to work with. Which algorithm is the best choice given these constraints?

With limited data and a mandate for simplicity, Naïve Bayes is appropriate; it trains quickly, has few parameters, resists over-fitting on small data, and yields interpretable probabilistic outputs suitable for purchase-propensity classification.

A. Ensembles increase complexity and often require larger data to avoid variance.
C. Neural networks need substantial data and tuning; overkill here.
D. Generative AI (e.g., GANs, LLMs) solves data creation or content generation, not straightforward binary classification.

1. MIT OCW 6.036, “Naïve Bayes for Text and Small-Data Problems,” Lecture 7 slides 3-5.

2. Cognilytica. “CPMAI v7 Methodology Handbook,” §5.3.1, p. 118 (algorithm selection criteria).

3. Bishop, C. “Pattern Recognition and Machine Learning,” Springer, 2006, §3.2, p. 45 (data requirements for Naïve Bayes).