Email Spam Detection

Requirements Clarification & Assessment

1. Understanding the Problem

   • Nature of the Problem: Email spam detection is a binary classification problem where the goal is to classify emails as either spam (1) or not spam (0).
   • Dataset Characteristics: Typically, the dataset is highly imbalanced, with a larger number of non-spam (ham) emails compared to spam emails.

2. Business Goals and Preferences

   • Precision Focus: If the priority is to minimize the chance of classifying important emails as spam (false positives), focus on precision.
   • Recall Focus: If the priority is to ensure all spam emails are captured, even if some non-spam emails are misclassified (false negatives), focus on recall.
   • Balance: If both false positives and false negatives are equally important, aim for a balance using the F1 score.

3. Evaluation Metrics

   • Accuracy: Not suitable for imbalanced datasets as it might be misleading.
   • Precision, Recall, F1 Score: More appropriate metrics for imbalanced datasets.
   • AUC/ROC: Useful for assessing the model's ability to distinguish between spam and non-spam across various thresholds.