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Data Interview Question

Why Naive Bayes is Widely Used for Spam Detection

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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.