I ran through the usual stuff: resampling, adjusting class weights, threshold tuning, picking the right metrics.
Start by clarifying the business context and evaluation metric, since imbalance handling depends on the cost of false positives vs. false negatives. Then discuss a combination of data-level techniques (resampling, augmentation) and algorithm-level techniques (class weights, threshold tuning), emphasizing that the right choice is empirical and tied to the product goal. Finally, mention monitoring and iteration to ensure the solution remains effective in production.
Pro tip: Avoid jumping straight to SMOTE; instead, first ask whether the imbalance reflects the real-world distribution and whether the model should optimize for ranking or calibration. Demonstrating that you consider the end-to-end system—not just the training set—sets you apart.
Ask about the business objective, the cost of different error types, and the evaluation metric (e.g., PR-AUC, F1, recall@k). This determines whether and how to handle imbalance.
Quantify the class ratio and check if the imbalance is intrinsic or due to sampling. Also assess whether the minority class is well-separated or noisy.
Consider data-level methods (oversampling, undersampling, SMOTE, augmentation) and algorithm-level methods (class weights, focal loss, threshold moving). Select based on model type and data size.
Use cross-validation with stratification, and evaluate on a holdout set that reflects the true distribution. Tune the decision threshold to align with business costs.
Track performance over time and watch for drift in class distribution. Be prepared to retrain or adjust the threshold as the data evolves.
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Start by defining each loss function mathematically and highlighting their sensitivity to outliers. Then compare their properties (differentiability, robustness, convergence) and discuss practical scenarios for choosing one, especially in the context of Applovin's ad-tech data which may contain outliers.
Pro tip: Mention that Huber loss combines the best of both worlds by being quadratic for small errors and linear for large errors, making it robust to outliers while still providing smooth gradients. Also, relate to Applovin's use case: ad click-through rates and revenue data often have heavy tails, so Huber is often preferred.
Provide the mathematical formulas: MAE = mean(|y - ŷ|), MSE = mean((y - ŷ)^2), Huber = mean(0.5*(y-ŷ)^2 if |y-ŷ|<=δ else δ*(|y-ŷ| - 0.5δ)).
Explain that MSE is highly sensitive to outliers due to squaring, MAE is robust but has constant gradient, and Huber is robust for large errors but smooth for small errors.
MSE is differentiable everywhere, MAE is not differentiable at 0 (subgradient), Huber is differentiable everywhere and combines smoothness with robustness.
Choose MSE when outliers are not a concern and you want fast convergence; MAE when outliers are prevalent and you want robustness; Huber when you want a balance and have some outliers but also want smooth gradients.
In ad-tech, data often has outliers (e.g., viral content, sudden spikes). Huber loss is often a good default, but MSE might be used if data is cleaned, and MAE if interpretability of median is desired.
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This one tripped me up more than I expected.
Start by explaining that asymmetric loss functions can be designed by weighting the loss differently for over-predictions versus under-predictions. Then, describe common approaches such as quantile loss, asymmetric squared error, or custom loss functions, and discuss how to choose the weighting based on the business objective. Finally, mention evaluation metrics and potential pitfalls like bias-variance trade-off.
Pro tip: Relate the choice of loss function to the specific business context at AppLovin, such as ad bidding where over-prediction might lead to overspending, and under-prediction to missed revenue. This shows you understand the practical implications.
Determine which direction (over- or under-prediction) should be penalized more and why, based on the business problem.
Select a standard loss like MSE or MAE as a starting point, then modify it to introduce asymmetry.
Apply a higher weight to the error term when the prediction is on the side you want to penalize more. For example, use a weight α for over-predictions and β for under-predictions, with α > β.
Mention that quantile loss (pinball loss) is a natural way to achieve asymmetry, where the quantile parameter controls the penalty.
Discuss how to tune the weighting parameter using validation data and evaluate with asymmetric metrics like asymmetric MAE or business KPIs.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Start by defining quantile regression as estimating conditional quantiles rather than the mean, then derive the pinball loss from the asymmetric absolute error minimization. Walk through how the loss weights positive and negative errors differently based on the target quantile, and connect it to practical uses like robust regression and prediction intervals.
Pro tip: Emphasize that the pinball loss is the tilted absolute value function and that its subgradient condition leads to the quantile as the minimizer, showing deep understanding beyond just the formula.
Explain that quantile regression models the conditional quantile of the response variable, offering a more complete view of the conditional distribution than mean regression.
State the pinball loss function: L_τ(y, ŷ) = (τ - 1_{y < ŷ}) * (y - ŷ), where τ is the target quantile. Highlight its asymmetric nature.
Show that minimizing the expected pinball loss yields the τ-th quantile. Derive by setting the subgradient to zero: E[(τ - 1_{Y < q})] = 0, leading to P(Y < q) = τ.
Describe how over-predictions (ŷ > y) are penalized by (1-τ) and under-predictions (ŷ < y) by τ, so the loss tilts toward the desired quantile.
Mention use cases like robust regression to outliers, prediction intervals, and handling heteroscedasticity, especially relevant in ad-tech for modeling uncertainty in user behavior.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.