How do I approach Machine Learning interview questions?

Machine Learning questions require understanding of core concepts and practice. PracHub provides solutions with explanations to help you master machine learning interviews.

What difficulty level is this interview question?

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

What role is this question designed for?

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

Address Overfitting in Supervised Learning Models

Last updated: Mar 29, 2026

Quick Overview

This question evaluates understanding of the bias–variance trade-off, overfitting, and model generalization within the Machine Learning domain. It is commonly asked to assess a candidate's ability to diagnose large train–test performance gaps and to connect conceptual trade-offs with practical application, testing both conceptual understanding and hands-on problem-solving skills.

Google

Jul 12, 2025, 6:59 PM

Data Scientist

Onsite

Machine Learning

Bias–Variance Trade-off and Reducing a Train–Test Performance Gap

Scenario

You are evaluating a supervised learning model and observe that training accuracy is significantly higher than test accuracy.

Question

Explain the bias–variance trade-off in supervised learning.
Your model performs significantly better on the training set than on the test set. What practical steps can you take to address this gap?

Hints

Relate variance to model complexity.
Consider regularization, cross-validation, simpler models, more data, early stopping, ensembling, and feature engineering.

Solution

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