Imbalanced datasets:
- Uneven representation of different classes or outcomes
- Can lead to poor performance on underrepresented classes
Inappropriate model selection:
- Choosing a model type that doesn't suit the problem or data characteristics
Inadequate preprocessing:
- Failing to handle outliers, missing data, or scale features appropriately
Overly complex models:
- Using unnecessarily sophisticated models that capture noise rather than true patterns
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