Detect Overfitting or Underfitting in Logistic Regression Models

Q: Detect Overfitting or Underfitting in Logistic Regression Models

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.

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Question

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)?

Detect Overfitting or Underfitting in Logistic Regression Models

Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction

Scenario

Question

Solution

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Detect Overfitting or Underfitting in Logistic Regression Models

Overview

Logistic Regression Bias–Variance in High‑Dimensional Ads Prediction

Scenario

Question

Solution

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