Model Evaluation and Validation Strategy Questions

Designing principled validation and evaluation approaches to accurately estimate generalization and support model selection. Topics include cross validation strategies for different data types, holdout and temporal validation for time dependent data, metric selection aligned to product goals, nested validation to avoid selection bias, techniques to avoid label leakage, calibration and confidence estimation, sample size and statistical power considerations, multiple comparison correction, and practical pipelines to automate validation and track model performance over time.