This started straightforward and then kept expanding.
Start by framing the problem: with only two features, the key is to extract maximum signal from each while avoiding overfitting. Walk through a logical pipeline: handle missing values, engineer numeric and categorical features, then encode and scale appropriately, justifying each choice with the model type and business context in mind.
Pro tip: Emphasize that with only two features, feature engineering is less about creating many features and more about making each feature as informative as possible—e.g., target encoding for neighborhood can capture local price levels, but must be done with cross-validation to avoid leakage.
Check for missing values and outliers in both features. Decide on imputation strategies (e.g., median for area, mode or 'missing' category for neighborhood) and document assumptions.
Transform area: consider log transform if skewed, create bins (e.g., small/medium/large) if non-linear relationships are suspected, and scale if using distance-based models.
Handle neighborhood: group rare categories, create frequency encoding, and apply target encoding (with cross-validation) or one-hot encoding depending on cardinality and model choice.
Explore interactions like area * neighborhood average price or area-to-neighborhood median ratio, which can capture location-specific price per square foot.
Assemble a reproducible pipeline (e.g., using sklearn's ColumnTransformer) that applies all steps, and validate with cross-validation to ensure no data leakage.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Ran through one-hot, target encoding, frequency encoding, and embeddings.
Start by acknowledging that high-cardinality categorical features like neighborhood require careful encoding to balance model performance and computational efficiency. Discuss common strategies such as target encoding, frequency encoding, and hashing, and compare their tradeoffs in terms of overfitting, interpretability, and scalability. Emphasize the importance of validation and domain context in selecting the right approach.
Pro tip: Mention that target encoding should be done within cross-validation folds to prevent leakage, and consider smoothing to handle categories with few samples. This shows practical awareness of common pitfalls.
Explain why high-cardinality features are problematic: increased dimensionality, risk of overfitting, and computational cost with one-hot encoding.
Describe options like target encoding, frequency/count encoding, hashing, and embeddings, and briefly explain how each works.
Discuss pros and cons: target encoding can leak and overfit but is compact; frequency encoding is simple but may lose information; hashing is scalable but loses interpretability; embeddings capture relationships but need data.
Tie the choice to the model (e.g., tree-based vs. linear) and data size, and mention the need for validation and possibly combining encodings.
Give a concrete recommendation for the scenario, such as using target encoding with smoothing and cross-validation for a tree-based model.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.
Linear regression as a baseline, gradient boosted trees as the real contender.
Start by clarifying the problem context and data characteristics, especially the house price distribution (likely skewed with outliers). Then propose a baseline model (e.g., linear regression) and justify why it's a good starting point, and discuss how the distribution informs the choice of evaluation metric (e.g., MAE vs. RMSE) and potential transformations.
Pro tip: Mention that you would first check for skewness and outliers, and consider using a log transformation of the target to stabilize variance and improve model performance. Also, emphasize that the choice of metric should align with business objectives, not just statistical convenience.
Ask about the dataset size, features, and the goal (e.g., prediction accuracy vs. interpretability). Confirm the house price distribution: is it right-skewed with expensive outliers?
Start with a simple, interpretable model like linear regression (or regularized variants) because it's fast, provides a baseline, and helps understand feature relationships. Mention that more complex models (e.g., gradient boosting) can be tried later if needed.
If the target is skewed, consider a log transformation to make the distribution more normal, which can help linear models and reduce the impact of outliers. Alternatively, use models robust to outliers (e.g., tree-based).
Given the skewed distribution, RMSE penalizes large errors more, which might be undesirable if expensive houses are outliers. MAE is more robust and interpretable as average absolute error. Also consider RMSLE if predicting log prices, or MAPE if relative error matters.
Use cross-validation to compare models and metrics. Align the metric with business impact: e.g., if overpredicting cheap houses is costly, use asymmetric loss. Communicate trade-offs clearly.
AI-generated suggestions, not part of the candidate's original notes. May be inaccurate — verify before relying on them.