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Address Overfitting in Supervised Learning Models

GoogleData Scientist · Onsite
MediumData ScientistOnsiteMachine Learning

Last updated

Evaluates supervised learning judgment around bias, variance, overfitting, and train-test performance gaps. Strong answers diagnose leakage, split errors, distribution shift, and high variance, then propose regularization, simpler models, validation, more data, and monitoring.

Scenario

Model evaluation in supervised learning where training accuracy exceeds 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 steps can you take to address this gap?

Hints

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