TikTok·Data Scientist·Technical Phone Screen·Senior

Senior

Jun 2026

Summary

TikTok data scientist interview that went deep on regularization theory. One question but it had three sub-parts and each one could've been its own conversation. Felt like a stats PhD oral exam more than a typical DS screen.

Questions Asked (1)

You have two nearly identical standardized predictors (correlation = 0.99), and you're given the X'X matrix and X'y vector. First, compute the ridge regression weights for a given lambda and explain why L2 regularization distributes weight across correlated features. Second, without solving LASSO explicitly, argue what coefficient pattern L1 regularization would likely produce under high collinearity and why. Third, propose an elastic-net setup with specific alpha and lambda values that balances sparsity with variance control, and explain how you'd tune those and which validation metric fits if you care about sparse models with minimal PR-AUC loss.

Technical Trade-offsAlgorithms & Data StructuresProduct Analytics & Metrics

Author's notes

This wrecked me a little.

Create a free account to read the full note

AI HintsAI Generated

Suggested Approach

Start by computing the ridge weights using the closed-form solution (X'X + λI)^(-1)X'y, emphasizing how the +λI term stabilizes the inverse and distributes weight among correlated features. Then contrast with LASSO's L1 penalty, explaining that it tends to select one feature and zero out the other due to the geometry of the constraint. Finally, propose an elastic-net model with a specific alpha (e.g., 0.5) and lambda (e.g., 0.1), and describe tuning via cross-validation using PR-AUC as the primary metric, possibly with a sparsity constraint.

Pro tip: When discussing regularization, always connect the math to practical implications: L2 reduces variance by shrinking coefficients, while L1 induces sparsity for feature selection. For TikTok, where sparse models aid interpretability and deployment, emphasize how elastic net balances these trade-offs.

1. Compute Ridge Regression Weights

Use the closed-form solution: β_ridge = (X'X + λI)^(-1)X'y. Explain that adding λ to the diagonal makes the matrix invertible even with perfect collinearity and shrinks coefficients proportionally to their variance.

2. Explain L2 Regularization's Effect on Correlated Features

L2 regularization distributes weight across correlated features because the penalty is quadratic: it encourages coefficients to be small and similar, effectively sharing the predictive power among them rather than arbitrarily selecting one.

3. Argue LASSO's Coefficient Pattern under High Collinearity

With L1 regularization, the penalty is linear, leading to sparse solutions. Under high collinearity, LASSO tends to select one feature arbitrarily and set the other to zero, because the constraint region has corners that promote zero coefficients.

4. Propose Elastic-Net Setup and Tuning Strategy

Suggest an elastic net with alpha=0.5 (equal mix of L1 and L2) and lambda=0.1 as a starting point. Tune alpha and lambda via grid search with cross-validation, using PR-AUC as the primary metric to minimize loss while monitoring sparsity (e.g., number of non-zero coefficients).

Key Points to Mention

Closed-form ridge solution: (X'X + λI)^(-1)X'y and its numerical stability.
L2 penalty's quadratic form leads to proportional shrinkage and weight distribution.
L1 penalty's linear form leads to sparse solutions and arbitrary feature selection under collinearity.
Elastic net combines L1 and L2, balancing sparsity and variance control.
Tuning alpha and lambda via cross-validation, using PR-AUC for imbalanced or sparse-focused tasks.
Trade-off between sparsity (interpretability, deployment) and predictive performance (PR-AUC).

AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.