A strong offline score doesn’t guarantee strong performance in production. When evaluation is flawed, metrics can look impressive while hiding real issues.

Common causes include subtle data leakage during preprocessing and test sets that don’t reflect real-world conditions. The model behaves exactly as trained, but the evaluation setup gave a false sense of confidence.

The previous chapter covered what to measure. This chapter focuses on how to measure it correctly. Getting evaluation right comes down to data splitting, handling time properly, and building datasets that match what the model will see in production.