Day 9 – Bayes’ Theorem & Naive Bayes Classifier

Hey everyone 👋 Dhairya here,

Today’s focus was on one of the most fundamental ideas in probabilistic machine learning: Bayes’ Theorem and its practical use in the Naive Bayes classifier.

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🔢 What I Learned Today

1. Bayes’ Theorem

• Formula:

  P(A∣B)=P(B∣A)⋅P(A)P(B)P(A|B) = \frac{P(B|A) \cdot P(A)}{P(B)}P(A∣B)=P(B)P(B∣A)⋅P(A)​

• Helps us update probabilities when new evidence is observed.

• Example: Diagnosing a disease given test results.

2. Naive Bayes Classifier

• Assumes features are independent (naive assumption).

• Surprisingly effective despite being simplistic.

• Variants:

  • Gaussian Naive Bayes → continuous data

  • Multinomial Naive Bayes → counts (e.g., word frequency in text)

  • Bernoulli Naive Bayes → binary features (yes/no, 0/1)

3. Applications

• Spam detection (classify email as spam/ham)

• Sentiment analysis (positive/negative review)

• Document classification

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🌱 Reflections

Naive Bayes showed me how simplicity can still be powerful. It’s lightweight, fast, and works shockingly well for text-based problems. I really liked experimenting with it on toy datasets.

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💻 Notebook

My Day 9 notebook is here 👉 GitHub Link – Day 9 Notebook

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📚 Resources

🎥 YouTube

• StatQuest – Naive Bayes

• Khan Academy – Bayes’ Theorem

• Simplilearn – Naive Bayes Classifier

🌐 Websites

• GeeksforGeeks – Naive Bayes Classifier

• Machine Learning Mastery – Naive Bayes

• Scikit-learn Documentation – Naive Bayes

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🎯 What’s Next?

For Day 10, I’ll start Exploring Scikit-Learn in more detail — how to use it for datasets and preprocessing.

See you tomorrow 👋
— Dhairya