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This is a medium difficulty Machine Learning question, commonly asked during Technical Screen rounds at Google.

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This question is commonly asked for Data Scientist candidates at Google during technical interviews.

Detect Overfitting or Underfitting in Logistic Regression Models

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of bias–variance trade-offs, model generalization and diagnostics for logistic regression applied to high‑dimensional, sparse, and imbalanced datasets, including feature sparsity and regularization considerations.

Google

Aug 4, 2025, 10:55 AM

Data Scientist

Technical Screen

Machine Learning

Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction

Scenario

You are building a large‑scale binary classifier (e.g., click/conversion prediction for Google Display ads) with hundreds to thousands of mostly sparse, high‑cardinality features (one‑hot categorials, text/ids, and some numerics). The dataset is large and exhibits class imbalance.

Question

In this setting, does logistic regression typically underfit or overfit? Describe the conditions that drive each outcome.
How would you detect underfitting vs overfitting in practice (e.g., learning curves, cross‑validation)?
What techniques would you use to address each case (consider regularization strength, feature selection, high‑dimensional sparsity, and related tooling)?

Solution

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