Model evaluation in supervised learning where training accuracy exceeds test accuracy.

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?

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

Google Data Scientist interview question: Address Overfitting in Supervised Learning Models. {"blocks": [{"key": "78caaede", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...