Why Naive Bayes is Widely Used for Spam Detection

Requirements Clarification & Assessment

1. Understand the Problem Domain

   • Objective: Classify emails into spam or non-spam (ham) categories.
   • Nature of Data: Textual data, often high-dimensional, with a mix of common and rare words.
   • Output: Binary classification (spam or ham).

2. Identify Algorithm Suitability

   • Algorithm: Naive Bayes
   • Key Features:
     • Probabilistic model based on Bayes' Theorem.
     • Assumes conditional independence between features.
     • Requires prior probabilities and likelihoods.

3. Evaluate Data Characteristics

   • Data Type: Categorical and discrete (presence or absence of words).
   • Data Distribution: Often imbalanced with more legitimate emails than spam.
   • Feature Relevance: Some words are more indicative of spam than others.

4. Performance Metrics

   • Accuracy: Overall correct predictions.
   • Precision and Recall: Especially important to minimize false positives.
   • F1 Score: Balances precision and recall.