Feature selection issues:
- Including irrelevant features that introduce noise
- Omitting important features that significantly impact the outcome
Data leakage:
- Inadvertently including information in training that wouldn't be available in real-world predictions
Concept drift:
- Changes in the underlying patterns or relationships over time
- Model becomes less accurate as conditions evolve
Sampling bias:
- Training data not representative of the full population or real-world scenarios
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