I went straight to gradient boosting and talked through why tree-based models tend to handle the mix of numeric and categorical features you get in real estate data pretty well.
Start by clarifying the problem context—data size, features, and business requirements—then propose a baseline model like linear regression and a more complex model like gradient boosting or a neural network. Explain the trade-offs between interpretability, accuracy, and scalability, and justify your final choice based on the specific scenario.
Pro tip: Mention that you would validate assumptions (e.g., linearity, independence) and consider feature engineering (e.g., location, square footage) before jumping to complex models. Also, discuss how you would handle outliers and missing data, as these are common in real estate datasets.
Ask about the dataset size, feature types, prediction goal (e.g., point estimate vs. distribution), and business constraints like interpretability or latency.
Suggest simple, interpretable models like linear regression or decision trees as a starting point to establish a performance benchmark.
Discuss more complex models like gradient boosting (XGBoost, LightGBM) or neural networks that can capture non-linear relationships and interactions.
Compare models on accuracy, interpretability, training time, and scalability. Explain why you might choose one over another based on the context.
State your preferred approach, justify it, and mention how you would iterate with feature engineering, hyperparameter tuning, and validation.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.