Spurious correlations show up when a model performs well in validation but fails under slight input changes.This happens when the model latches onto shortcuts in the data—background artifacts, metadata, or proxy features—rather than the true signal.

You’ll often see brittle behavior when conditions change.Use counterfactual testing: modify or remove suspected features and observe prediction changes. Training with more diverse data and applying regularization also helps reduce shortcut learning.

Common mistakes:

1. Trusting aggregate metrics without stress tests

2. Training on overly clean or curated datasets

3. Ignoring feature importance analysis

Robust models should fail gracefully, not catastrophically.