I jumped straight to overfitting because 'high-dimensional' feels like a trap toward regularization talk, but the interviewer pushed back and asked about underfitting too.
Start by clarifying that logistic regression is a relatively low-variance, high-bias model, so with hundreds or thousands of features it is more prone to underfitting than overfitting, but the actual outcome depends on sample size, feature quality, and regularization. Then systematically discuss the conditions that drive each, how to detect them, and the remedies, emphasizing that the right answer is context-dependent.
Pro tip: Frame the answer around the bias-variance trade-off and mention that with large-scale ad data, the main risk is underfitting due to the linear decision boundary, not overfitting—unless the feature-to-sample ratio is extreme or features are highly collinear. This shows you understand both theory and practical scale.
State that logistic regression is a linear model with high bias and low variance, and that its behavior depends on the number of training examples (n) relative to features (p), feature multicollinearity, and regularization strength.
Explain that underfitting occurs when the true relationship is non-linear, when features are weak or noisy, when regularization is too strong, or when n is very large relative to p—common in ad performance prediction where interactions and non-linearities matter.
Explain that overfitting can happen when p is large relative to n (e.g., thousands of sparse features with limited data), when features are highly correlated, or when regularization is too weak, leading to unstable coefficients.
Describe using learning curves (training vs. validation error), cross-validation, and monitoring coefficient magnitudes and variance. For underfitting, both errors are high and close; for overfitting, training error is low but validation error is high.
For underfitting: add non-linear features/interactions, use polynomial terms, reduce regularization, or switch to a more flexible model. For overfitting: increase regularization (L1/L2), reduce dimensionality, gather more data, or use feature selection.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.