ML Case: Missing Lowest-Income Bracket in California Housing Data

Context

You're building a supervised model (regression) to predict California housing prices using a dataset similar to the classic California Housing data. One key covariate is household income. The training data contains no observations from the lowest-income bracket (< $25k), but the deployed model must perform well across all income ranges, including this unseen bracket at inference time.

Assume the deployment/test distribution will include the full income range, including < $25k. You may optionally have access to unlabeled production covariates (features only) that include the missing bracket.

Task

Design a modeling approach that achieves robust performance across all income ranges, with special attention to the unseen lowest-income bracket. Your answer should cover:

1. Diagnostics: How you’d confirm and quantify the shift and missing support.
2. Modeling strategy: Architectures/algorithms that extrapolate sensibly and incorporate domain knowledge.
3. Distribution shift handling: Methods such as importance weighting, domain adaptation/transfer learning, and data augmentation (if appropriate).
4. Feature scaling and preprocessing choices that help stability.
5. Validation: How you will evaluate performance for the unseen bracket before production, stress tests, and uncertainty estimates.
6. Deployment and incremental retraining plan once data from the missing bracket starts arriving.

You may reference techniques like domain similarity, incremental retraining, covariate shift correction, transfer learning, and feature scaling.