Choosing a Meta-Classifier for Stacking

Requirements Clarification & Assessment

1. Objective:

   • Develop a spam detection system to accurately classify emails as spam or non-spam.

2. Current Status:

   • Existing models (SVM and Random Forest) have not achieved desired accuracy.

3. Constraints:

   • Must improve upon current model accuracy.
   • Should efficiently handle high-dimensional data typical in text classification.

4. Desired Outcome:

   • A more accurate prediction model using a stacking approach with an optimal meta-classifier.

5. Considerations:

   • Computational efficiency and model interpretability.
   • Ability to handle overfitting.
   • Compatibility with base models (SVM, Random Forest).
   • Scalability to large datasets typical in email systems.