Generative and Discriminative Models

Requirements Clarification & Assessment

1. Objective:

   • Develop a classifier to distinguish between spam and non-spam emails.
   • Focus is on accurate classification, not generating new data.

2. Data Availability:

   • Large dataset of labeled emails (spam or not spam).
   • Assumption: The dataset is representative of the problem space.

3. Performance Metrics:

   • Prioritize metrics like accuracy, precision, recall, and F1-score.
   • Consider the cost of false positives (non-spam classified as spam) and false negatives (spam classified as non-spam).

4. Model Requirements:

   • Must handle high-dimensional input data (emails with various features).
   • Should be efficient in training and prediction due to large dataset size.

5. Constraints:

   • Computational resources and time for training and prediction should be considered.
   • The model should be interpretable to some extent for debugging and improvement purposes.