Detect Overfitting or Underfitting in Logistic Regression Models

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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.

Question

Detect Overfitting or Underfitting in Logistic Regression Models

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)?

Clarifying Questions to Ask Guidance

Clarify the task, data shape, labels, constraints, and evaluation metric.
State assumptions behind the math or modeling technique you choose.
Connect theory to practical training, debugging, and deployment implications.

What a Strong Answer Covers Guidance

Correct definitions and formulas where the prompt requires them.
A practical explanation of how the method behaves on real data.
Trade-offs, failure modes, diagnostics, and mitigation strategies.
Evaluation choices that match the product or modeling objective.

Follow-up Questions Guidance

How would noisy labels, class imbalance, or distribution shift affect the answer?
What would you monitor after deployment?
Which baseline would you compare against first?

Detect Overfitting or Underfitting in Logistic Regression Models

Quick Overview

Detect Overfitting or Underfitting in Logistic Regression Models

Detect Overfitting or Underfitting in Logistic Regression Models

Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction

Scenario

Question

Clarifying Questions to Ask Guidance

What a Strong Answer Covers Guidance

Follow-up Questions Guidance

Write your answer