Start by framing CTR prediction as a binary classification problem with imbalanced data, then walk through a progression from a trivial baseline (e.g., predicting the global average CTR) to logistic regression, random forest, and gradient-boosted trees, justifying each choice based on bias-variance, interpretability, and compute cost. Explicitly address why you skipped SVMs, Naive Bayes, and simple neural nets, tying each to scalability, feature interactions, or latency constraints.
Pro tip: Emphasize that at Reddit's scale, inference latency and retraining cost often trump marginal AUC gains, so you'd benchmark models not just on offline metrics but also on serving throughput and memory footprint. Mention that you'd consider a simple neural net (e.g., a small MLP) only after exhausting tree-based methods, and only if you have the infrastructure to serve it efficiently.
Frame CTR prediction as binary classification with imbalanced classes, and establish a trivial baseline (e.g., global average CTR or a heuristic) to set a performance floor and sanity-check metrics.
Explain why logistic regression is a strong, interpretable, and cheap first model; why random forest captures non-linearities and interactions with moderate tuning; and why gradient-boosted trees often win on tabular data by sequentially correcting errors.
Compare the models on bias-variance (LR high bias, RF/GBT lower bias but higher variance if not tuned), interpretability (LR coefficients vs. tree feature importances vs. SHAP), and compute cost (training and inference latency, memory).
Argue why SVMs are impractical for large-scale sparse data (O(n^2) training, poor probability calibration), Naive Bayes assumes feature independence (violated by user-item interactions), and simple neural nets may be overkill without significant feature engineering or infrastructure for low-latency serving.
Recommend starting with logistic regression for a quick baseline, then moving to GBTs if gains justify the cost, and only exploring neural nets if you have the data scale and serving infrastructure to support them.
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Went with stratified k-fold and mentioned time-based splits as an alternative if the data has temporal structure, which felt right for a feed ranking use case.
Start by outlining a general cross-validation strategy that balances bias-variance tradeoff and computational cost, then tailor it to each model's characteristics. For each model, specify the hyperparameters to tune and justify choices based on model complexity and data size.
Pro tip: Emphasize that cross-validation should be nested when hyperparameter tuning is involved to avoid optimistic bias, and mention that for large-scale systems like Reddit, you might use a holdout set for final evaluation to save compute.
Select k-fold cross-validation (e.g., k=5 or 10) for general use, but consider stratified k-fold for imbalanced classification or time-series split for temporal data. Mention that for large datasets, a single validation set might be used to reduce computation.
Tune regularization strength (C) and penalty type (L1/L2). Use cross-validation to select the best C, and consider feature scaling within the pipeline to avoid data leakage.
Tune key hyperparameters like n_estimators, max_depth, learning_rate (for boosting), and subsample. Use cross-validation with early stopping for boosting models to prevent overfitting.
Tune architecture (number of layers, units), dropout rate, learning rate, and batch size. Use a validation set for early stopping and consider k-fold cross-validation if data is limited.
Use the same cross-validation folds across models for fair comparison, and report mean and standard deviation of the performance metric. Consider nested cross-validation for unbiased performance estimation.
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ROC AUC was my first answer and I think that was fine.
Start by defining the problem context and the cost of different errors, then select metrics that align with those costs. For balanced data, justify accuracy, precision, recall, F1, and AUC-ROC, explaining when each is appropriate. For imbalanced data, discuss why accuracy fails and shift to metrics like precision-recall AUC, F1, and MCC, and mention techniques like resampling or class weighting.
Pro tip: Always tie metric choice to business impact—e.g., in a spam filter, false positives (marking important email as spam) are worse than false negatives. This shows you think beyond technical metrics.
Ask about the specific task, the cost of false positives vs. false negatives, and whether probabilities or hard labels are needed.
Recommend accuracy as a baseline, but also precision, recall, F1, and AUC-ROC. Explain that with balanced classes, these metrics are more reliable and interpretable.
Show that a naive model predicting the majority class can achieve high accuracy but poor minority class performance, making accuracy misleading.
Suggest precision-recall AUC, F1-score, and Matthews correlation coefficient (MCC). Emphasize that PR AUC is more informative than ROC AUC when the positive class is rare.
Briefly note that metric choice may pair with techniques like resampling, class weighting, or threshold tuning to improve minority class detection.
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Blanked for a second and then just listed things: feature engineering on user history, embedding categorical IDs, calibration layer on top of the model, online learning or periodic retraining, and monitoring for distribution shift.
Acknowledge the current model's strengths and limitations, then prioritize improvements based on impact and feasibility. Structure your answer around a clear framework that covers data, modeling, evaluation, and deployment, tying each improvement to business or user value.
Pro tip: Frame improvements as hypotheses with expected outcomes and metrics, showing you think like an owner. Also, mention trade-offs and how you'd validate each change before full rollout.
Briefly summarize the model's current performance and known weaknesses from evaluation and error analysis. This sets the stage for targeted improvements.
Rank potential improvements by expected business impact, user value, and implementation cost. Focus on high-impact, low-effort wins first.
Detail 2-3 concrete enhancements across data, features, model architecture, or training. Explain how each addresses a limitation and its expected effect.
Describe how you would test each improvement (offline metrics, online A/B tests) and the success criteria. Mention potential risks and mitigation.
Tie the improvements back to Reddit's objectives, such as increasing engagement, improving content relevance, or reducing latency.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.