How do you evaluate a regression model?
Interviewer Expectations for this Question
01
Core Competency
Assesses fundamental understanding of Machine Learning conventions, runtime behavior, and memory/performance considerations.
02
Evaluation Criteria
Hiring managers look for precision, avoidance of ambiguous jargon, and ability to explain trade-offs under real production conditions.
Comprehensive Model Answer
Verified Solution
Choose metrics that match the problem.
- MAE: mean absolute error, in the target's units, robust to outliers.
- MSE and RMSE: penalize large errors more; RMSE is in target units and sensitive to outliers.
- R-squared: fraction of variance explained, but it always rises with more features, so use adjusted R-squared.
- MAPE: percentage error, useful across scales but undefined when the actual is zero.
from sklearn.metrics import mean_absolute_error, root_mean_squared_error, r2_score
Always compare against a baseline, such as predicting the mean or the previous value. Inspect residual plots for patterns, heteroscedasticity and non-linearity, and check errors across segments, not just overall. Use cross-validation for stable estimates and a held-out test set for the final report.
Candidate Response Strategy & Interview Tips
- Start with a concise one-sentence summary: Deliver a direct, confident answer first before expanding into nuances.
- Demonstrate real-world trade-offs: Discuss where this approach excels and when you would avoid it in production systems.
- Discuss complexity & edge cases: Proactively explain time/space complexity or boundary conditions (null values, scale limits).
- Prepare for interviewer follow-ups: Technical hiring panels frequently probe deeper into concurrency, backward compatibility, or alternative libraries.