Address Overfitting in Supervised Learning Models

Q: Address Overfitting in Supervised Learning Models

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.

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Question

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.

Address Overfitting in Supervised Learning Models

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

Scenario

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Address Overfitting in Supervised Learning Models

Overview

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

Scenario

Question

Hints

Solution

Comments (0)