1. Data Quality High-quality data is essential for accurate model validation. Issues such as missing values, outliers, and data entry errors can significantly affect the performance of the model.
2. Overfitting Overfitting occurs when the model performs well on the training data but poorly on unseen data. Techniques like cross-validation and regularization can help in mitigating overfitting.
3. Imbalanced Datasets In email marketing, certain events (like conversions) may be rare compared to others (like opens). Imbalanced datasets can lead to biased models. Techniques like resampling, cost-sensitive learning, and anomaly detection can be used to address this issue.