Model-selection discussion for a binary classification problem with limited data and potential non-linearities.

What is logistic regression, and what is its loss function? When can logistic regression outperform a random forest? Explain L1 and L2 regularization and their effects. How would you detect and mitigate overfitting in logistic regression? Compare Random Forest and Boosting (e.g., Gradient Boosting) in terms of bias, variance, interpretability, and typical use cases.

Cover convex optimization, feature sparsity, bias–variance trade-off, interpretability, ensemble diversity.

Google Data Scientist interview question: Compare Logistic Regression and Random Forest in Limited Data Scenarios. {"blocks": [{"key": "afbb9096", "text": "Scenario", "type": "header-two", "depth": 0, "inlineStyleRanges": [], "entityRanges": [], "data": {...