Start by acknowledging the extreme imbalance and sparse high-cardinality features, then propose two model families: (1) tree-based ensembles with techniques like scale_pos_weight or focal loss, and (2) regularized linear models with hashing or embeddings. For encoding, use out-of-fold target encoding or frequency encoding with smoothing, and design a time-aware cross-validation that respects the 24-hour label delay by using a holdout period or time-series splits.
Pro tip: Emphasize that the 24-hour label delay introduces a feedback loop: using recent data for validation can leak future clicks. Propose a 'label maturation' window and simulate production by training on data older than 24 hours and validating on the most recent matured data.
Propose two families: (1) Gradient boosted trees (e.g., XGBoost/LightGBM) with scale_pos_weight or focal loss, and (2) Regularized logistic regression with feature hashing or embeddings for high-cardinality features. Mention that tree models handle sparsity well, while linear models with hashing are scalable.
For ad_id and campaign_id, avoid one-hot encoding due to dimensionality. Use out-of-fold target encoding with smoothing (e.g., additive smoothing) or frequency encoding. Alternatively, use learned embeddings via a neural network. Ensure encoding is computed only on training folds to prevent leakage.
Design a time-based cross-validation scheme that accounts for the 24-hour label delay. Use a holdout set of the most recent data that has fully matured (i.e., older than 24 hours) for validation, and train on data prior to that. Alternatively, use a sliding window approach where the validation set is always at least 24 hours behind the training set.
Given the 0.7% positive rate, use metrics like AUC-ROC, PR-AUC, and log loss. Calibrate probabilities if needed. Discuss the trade-off between precision and recall and how it aligns with business goals (e.g., cost per click).
Mention that target encoding must be done within CV folds to avoid leakage. Also, ensure that features are available at prediction time and that the model can handle new ad_ids/campaign_ids via hashing or embeddings. Discuss monitoring for concept drift.
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Synthetic oversampling on sparse, high-cardinality feature spaces is a bad idea and I said so pretty quickly.
Start by framing the problem: severe class imbalance affects both model training and evaluation. Then compare each method across two dimensions: ranking performance (e.g., AUC, PR-AUC) and calibration (reliability of predicted probabilities). Finally, discuss when to avoid synthetic oversampling, emphasizing risks like overfitting and distribution shift.
Pro tip: At Uber, where decisions often rely on calibrated probabilities (e.g., pricing, fraud), emphasize that thresholding and calibration are critical, and that synthetic oversampling can distort probability estimates, making it risky for production.
Clarify that ranking performance measures how well the model orders positives above negatives (e.g., AUC, PR-AUC), while calibration measures how well predicted probabilities match observed frequencies (e.g., reliability diagrams, Brier score).
Class weighting adjusts the loss to penalize minority class errors more. It can improve ranking by focusing on minority class, but may distort calibration because predicted probabilities become biased toward the minority class.
Focal loss down-weights easy examples and focuses on hard ones. It often improves ranking for the minority class but can harm calibration, as probabilities may become overconfident or underconfident depending on the focusing parameter.
Undersampling balances the training set by removing majority class examples. It can improve ranking by reducing bias toward majority, but may discard useful information and lead to poor calibration due to altered prior probabilities.
Calibrated thresholding involves post-hoc calibration (e.g., Platt scaling, isotonic regression) and then selecting a threshold. It preserves ranking (if monotonic) and improves calibration, making it ideal when probabilities are used for decision-making.
Avoid synthetic oversampling (e.g., SMOTE) when the minority class is not well-separated or when synthetic samples could introduce noise, leading to overfitting and poor generalization. Also avoid when calibration is critical, as synthetic samples can distort probability estimates.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
ROC-AUC inflates at low prevalence because it includes a huge number of true negatives that are easy to get right.
Start by explaining why ROC-AUC and PR-AUC diverge at low prevalence, emphasizing that PR-AUC is more sensitive to false positives and thus more informative for imbalanced problems. Then argue that for ad CTR prediction, Model B is preferable because its higher PR-AUC indicates better precision-recall trade-off at the operating point that matters. Finally, describe how to use a cost matrix to choose a threshold that minimizes total expected cost, balancing missed clicks and wasted impressions.
Pro tip: Mention that the choice of metric should align with the business objective: for ad CTR, where positive class is rare and the cost of false positives (wasted impressions) is significant, PR-AUC is the more reliable metric. Also, note that the optimal threshold depends on the specific cost ratio and can be found by minimizing expected cost on a validation set.
Describe how ROC-AUC uses both true positive rate and false positive rate, which can be misleading when negatives dominate. PR-AUC focuses on the positive class, so it better reflects performance when prevalence is low.
Argue that Model B is more trustworthy because its higher PR-AUC means better precision at relevant recall levels, which is crucial for ad CTR where false positives (wasted impressions) are costly.
Assign costs: C_FN for missed clicks (lost revenue) and C_FP for wasted impressions (lost opportunity and potential user annoyance). Typically, C_FN > C_FP but both matter.
For each threshold, compute expected cost = C_FN * FN + C_FP * FP on a validation set. Choose the threshold that minimizes this cost, possibly subject to business constraints.
Validate the chosen threshold on holdout data and monitor performance over time, adjusting as costs or prevalence change.
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Platt scaling assumes a sigmoid relationship between raw scores and probabilities, so it works better when the model is roughly monotone but miscalibrated in a smooth way.
Start by comparing isotonic regression and Platt scaling in terms of flexibility, data requirements, and overfitting risk, then explain how to choose between them based on dataset size and calibration curve shape. Next, describe how to select a threshold by optimizing the desired metric (F1 or expected profit) using validation data, and finally detail the computation of precision at the top 1% of scores and its use in model comparison.
Pro tip: Emphasize that calibration should be evaluated on a held-out set and that business metrics like expected profit often require incorporating costs and benefits, which may lead to thresholds very different from those that maximize F1.
Discuss isotonic regression as a non-parametric, flexible method that requires more data but can capture complex relationships, versus Platt scaling as a parametric, sigmoid-based method that works well with small data but assumes a specific shape.
Use reliability diagrams and metrics like Brier score or log loss to evaluate calibration on a validation set, and consider the bias-variance trade-off when choosing between the two methods.
For F1, compute precision and recall across thresholds and pick the one that maximizes the harmonic mean; for expected profit, define a profit function that incorporates true positive benefit and false positive cost, then choose the threshold that maximizes expected profit on validation data.
Rank predictions by score, take the top 1% highest scores, and calculate the proportion of true positives among them; this measures the model's ability to identify the most likely positives.
Use precision at top 1% as a business-relevant metric to compare models, especially when the application requires high precision in the highest-scoring segment, and combine it with other metrics for a holistic view.
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Feedback loops were the part I hadn't thought through carefully enough before this interview.
Start by outlining a randomized bucket test where users are split into treatment (model top-k targeting) and control (baseline targeting), with CTR as the primary metric. Then detail the logging infrastructure needed to capture model scores, user features, and outcomes for drift and delay detection. Finally, explain safeguards like exploration, delayed feedback handling, and data hygiene to prevent feedback loops.
Pro tip: Emphasize that in top-k targeting, the treatment group only sees top-k items, so you must log the full ranking to avoid selection bias and ensure unbiased evaluation of the model's lift.
Randomly assign users to treatment (model top-k) and control (baseline) buckets, ensuring consistent assignment and sufficient power. Define success metrics (CTR lift) and guardrail metrics (e.g., revenue, user satisfaction).
Log model scores, user features, item features, top-k selections, and outcomes (clicks, impressions) with timestamps. Include request IDs to join logs across systems and capture the full ranking for unbiased analysis.
Monitor feature distributions (e.g., PSI, KL divergence) and prediction distributions over time to detect drift. Track label arrival times and use techniques like delayed feedback modeling or imputation to handle label delay.
Introduce exploration (e.g., epsilon-greedy) to collect unbiased data, use inverse propensity scoring (IPS) to correct for selection bias, and maintain a holdout set to evaluate model performance without feedback contamination.
Analyze test results with proper statistical methods (e.g., sequential testing), check for drift and delay impact, and iterate on model and logging based on findings.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.