The 60-second answer
Random Forest reduces variance through bootstrap aggregation plus random feature subsampling; boosting builds trees sequentially to correct residual errors. Compare bias/variance, training parallelism, sensitivity to hyperparameters/noise, interpretability, and inference cost.
Build the answer in this order
Random Forest reduces variance through bootstrap aggregation plus random feature subsampling; boosting builds trees sequentially to correct residual errors.
Compare bias/variance, training parallelism, sensitivity to hyperparameters/noise, interpretability, and inference cost.
Tune tree depth/leaves, sampling, learning rate or number of trees using held-out validation and inspect calibration.
Prefer a baseline that matches the tabular-data scale and product constraints rather than assuming one ensemble always wins.
A useful interview mental model
This is the shape of a strong answer—not a script to memorize.
Senior-level signal
- Explain why decorrelation is essential for bagging variance reduction and why boosting can overfit noisy residuals.
- Include memory, latency, calibration, and drift monitoring in a production comparison.
What the interviewer is really testing
Likely follow-up questions
Common weak-answer patterns
- Reciting a definition without mechanism or assumptions.
- Claiming one technique is always better without a data regime.
- Stopping before failure modes, validation, or deployment implications.