How do you evaluate the performance of a machine learning model?
Tests knowledge of evaluation metrics and the ability to choose the right metric for different problem types.
Why Interviewers Ask This
Accuracy is not always the best metric. Interviewers want to see if you understand precision, recall, F1-score, or RMSE depending on the cost of errors in the specific business context.
How to Answer This Question
Mention that the choice of metric depends on the problem type. Discuss confusion matrices for classification and MAE/RMSE for regression. Highlight the importance of validating on a hold-out set to avoid overfitting.
Key Points to Cover
- Select metrics based on problem type
- Consider cost of errors
- Validate on test sets
Sample Answer
For classification, I use precision and recall to balance false positives and negatives, especially if one error type is costlier. For regression, I rely on RMSE to penalize larger errors. I always ensure the model is evaluated on a separate test set to verify generalization. Cross-validation is also crucial to ensure stability across different data splits.
Common Mistakes to Avoid
- Relying solely on accuracy
- Ignoring class imbalance
- Evaluating on training data only
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