CPMAI_V7 practice question 1 of 35

You're working with an inexperienced team and this is all their first AI project. You're trying to work on a supervised learning binary classification problem to determine if emails are spam or not. What is the best approach for this project?

Full set: 100 questions

Answer options

A: Pick a simple algorithm such as naive bayes Correct answer

B: Pick a neural network algorithm since you know this works well for supervised learning approaches

C: Pick an ensemble method since you're not sure which algorithm will perform best

D: Pick a simple algorithm such as Gaussian mixture

Correct Answer:

A. Pick a simple algorithm such as naive bayes

Explanation

For an inexperienced team facing a straightforward binary text-classification problem, starting with a simple, well-understood baseline such as Naïve Bayes minimizes complexity, speeds experimentation, and still delivers competitive performance on spam filtering tasks.

Why Incorrect

B. Neural networks demand larger data, tuning and compute; unnecessary overhead for a first project.
C. Ensembles add complexity and interpretability issues; best used after single-model baselines underperform.
D. Gaussian Mixture Models are unsupervised density estimators, not an appropriate first choice for supervised spam classification.

References

1. CPMAI v7 Guide, Stage 4 “Initial Model Selection”, pp. 92-94.

2. MIT OCW 6.864 NLP, Lec 2 slides 15-16: Naïve Bayes as baseline for spam.

3. Sebastiani, F. “Machine Learning in Automated Text Categorization”, ACM CS 34(1), pp. 9-10, https://doi.org/10.1145/505282.505283.

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