Identify a classic bagging algorithm
C3 AIData Scientist · Technical Screen
Last updated
This interview question evaluates core ML concepts, assumptions, math intuition, training/evaluation trade-offs, and practical failure modes in a realistic interview setting. A strong answer for Identify a classic bagging algorithm states assumptions, handles edge cases, explains trade-offs, and shows how to validate the result clearly.
Identify a classic bagging algorithm
Multiple choice: Bagging
Which of the following algorithms is a classic example of bagging (bootstrap aggregating)?
A. Random Forest B. Gradient Boosting C. Logistic Regression D. Support Vector Machine
Clarifying Questions to Ask
- Clarify the task, data shape, labels, constraints, and evaluation metric.
- State assumptions behind the math or modeling technique you choose.
- Connect theory to practical training, debugging, and deployment implications.
Follow-up questions
- How would noisy labels, class imbalance, or distribution shift affect the answer?
- What would you monitor after deployment?
- Which baseline would you compare against first?
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