Structure your answer around a clear offline evaluation pipeline: start with data splitting and leakage prevention, then define metrics and choose K, and finally address advanced topics like IPS and its pitfalls. Emphasize that offline evaluation is a proxy for online performance, so you must carefully design to avoid biases and ensure defensibility.
Pro tip: Mention that you would validate your offline protocol by checking correlation with historical A/B tests or by running a small online experiment to calibrate, showing you understand the limitations and the need for continuous validation.
Use time-based splitting to simulate real-world deployment, ensuring no future data leaks into training. Consider user-level splitting if interactions are user-specific, and remove any features that would not be available at inference time.
Choose metrics that align with business goals, such as precision@K, recall@K, NDCG, or MAP. Pick K based on the number of items users typically interact with (e.g., top-10 for a homepage) and consider multiple K values to assess robustness.
Address selection bias by using techniques like inverse propensity scoring (IPS) or doubly robust estimation. Be aware of IPS pitfalls when propensities are small, such as high variance and instability, and consider clipping or using SNIPS.
Validate the offline protocol by checking correlation with historical A/B tests if available. Perform sensitivity analysis on K, metrics, and model hyperparameters to ensure conclusions are robust.
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Start by clearly writing out the confusion matrix and the formulas for each metric, then compute them step by step. After obtaining the numbers, interpret them in the context of the business goal—likely minimizing false positives to ensure only relevant items are shown—and explain why accuracy is misleading due to class imbalance. Conclude by recommending precision as the most appropriate metric, possibly with a trade-off discussion.
Pro tip: Always relate the metrics back to the product goal: for a system that shows only relevant items, false positives are costly (user annoyance), so precision is key. Mention that you might also consider precision@k or a weighted metric if the cost of FP is much higher than FN.
Write down the given TP, FP, TN, FN counts and state the formulas for precision, recall, specificity, F1, and accuracy.
Plug in the numbers and calculate precision, recall, specificity, F1, and accuracy. Show your work to demonstrate rigor.
Discuss class imbalance: if negatives dominate, a model predicting all negatives can have high accuracy but fail to identify positives. Accuracy doesn't distinguish between types of errors.
Argue that precision is most relevant because the goal is to show only relevant items (minimize false positives). Optionally, mention F1 if both precision and recall are important, but precision aligns with the stated objective.
Acknowledge that optimizing precision may reduce recall, and suggest monitoring both. Mention threshold tuning and business impact of FP vs FN.
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Reliability diagrams came to mind immediately.
Start by defining calibration and explaining why it matters for threshold-based decisions, then walk through diagnostic methods (reliability diagrams, calibration curves, Brier score) and fixes (Platt scaling, isotonic regression, or retraining with proper loss). Emphasize that calibration ensures predicted probabilities reflect true likelihoods, which is critical when choosing a threshold to balance business costs and benefits.
Pro tip: Always validate calibration on a holdout set and consider the business impact of miscalibration—e.g., if you threshold at 0.5 but the model is overconfident, you may reject good customers or approve bad ones, directly affecting revenue and risk.
Explain that a calibrated model's predicted probabilities match observed frequencies (e.g., among predictions of 0.7, about 70% should be positive). Highlight that calibration is separate from discrimination (AUC) and crucial for threshold-based decisions.
Use reliability diagrams (calibration curves) to visualize predicted vs. observed probabilities, and compute metrics like Brier score or log loss. Also check for overconfidence/underconfidence by binning predictions and comparing to actual outcomes.
For post-hoc calibration, use Platt scaling (logistic regression on scores) or isotonic regression (non-parametric, more flexible but needs more data). Alternatively, retrain with a proper scoring rule (e.g., log loss) and ensure no data leakage.
Evaluate calibration on a holdout set using reliability diagrams and metrics. Implement ongoing monitoring because calibration can drift over time as data distributions change.
Explain that when using a threshold (e.g., approve if p > 0.5), miscalibration directly impacts the expected cost/benefit. For example, if the model is overconfident, you might approve too many risky cases; if underconfident, you might reject profitable ones.
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Went with interpretability, fast iteration, and the fact that calibration is more natural out of the box.
Start by framing logistic regression as a strong baseline that offers interpretability, speed, and robustness for recommendation scoring, especially when data is limited or latency matters. Then, acknowledge its limitations by describing two failure modes and concrete mitigation strategies, showing you understand trade-offs and can make pragmatic choices.
Pro tip: Emphasize that at Meta's scale, a simple model that can be trained quickly and deployed with low latency often beats a complex model that's marginally better offline but harder to maintain. Mention that logistic regression can be a stepping stone to more complex models, and you'd only upgrade if the lift justifies the cost.
Explain why logistic regression is suitable: interpretability, fast training/inference, low latency, works well with sparse features, and provides calibrated probabilities. Highlight that it's a strong baseline for recommendation scoring.
Describe how logistic regression assumes linearity in the log-odds, so it may underperform when feature interactions or non-linear patterns are important. Propose solutions like feature engineering (e.g., crosses, bucketization) or using a more complex model if needed.
Explain that logistic regression can be biased toward the majority class when positive events (e.g., clicks) are rare. Suggest techniques like resampling, class weighting, or using precision-recall AUC for evaluation.
Briefly mention other failure modes like multicollinearity or outliers, and how to handle them (regularization, robust scaling). Show awareness of model diagnostics.
Summarize that the choice depends on the problem constraints: if interpretability and speed are critical, logistic regression is preferred; if performance gains from complex models are significant, consider upgrading, but always start simple.
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Graph-clustered splits are the right answer for network interference but I had to think out loud for a bit before getting there.
Start by defining the leakage problem: friend-derived features create dependencies between training and test examples, so random splits overestimate performance. Then compare time-based, user-disjoint, and graph-clustered splits on how well they mimic production, their bias-variance tradeoffs, and their impact on evaluation metrics.
Pro tip: Emphasize that the right split depends on the deployment scenario: if the model will be used on new users, user-disjoint is essential; if it will be used on future interactions of existing users, time-based is more appropriate. Also mention that graph-clustered splits can better preserve community structure but may reduce training data diversity.
Explain that friend-derived features (e.g., friend embeddings, activity counts) create dependencies between examples, so random splits leak information and inflate test performance.
Outline time-based (split by timestamp), user-disjoint (split by user ID), and graph-clustered (split by community detection) splits, and how each prevents leakage differently.
Compare the splits on realism, bias, variance, and computational cost. For example, time-based mimics temporal drift but may not generalize to new users; user-disjoint tests generalization to unseen users but may ignore temporal patterns; graph-clustered preserves community structure but can be complex to implement.
Suggest which split to use depending on the production scenario (e.g., new user vs. existing user, real-time vs. batch) and mention that a combination (e.g., time-based within user-disjoint) might be needed.
Propose evaluating the model on multiple splits to understand performance sensitivity and to detect leakage, and using online A/B tests as the ultimate validation.
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