Naive Bayes Classifiers
Build a fast text classifier from counts and Bayes rule, and know its blind spots
A taste of a lesson
Why does my naive Bayes spam filter say 0.9999 spam for almost everything it flags?
That is a known quirk. Naive Bayes treats every word as independent evidence. In a real spam email, words like 'free', 'offer' and 'click' tend to appear together, so the model counts what is really one signal several times. Multiplying all those likelihoods pushes the result towards 0 or 1. The ranking of messages is often still good, but the probability itself is overconfident. If you need trustworthy probabilities, calibrate on validation data. Can you think of two words in your data that almost always appear together?
Written by the teacher as an example. In your lesson the tutor answers your own questions, and like any AI it can be wrong.
What you will be able to do
- Apply Bayes rule to classify a short message by hand
- Explain the conditional independence assumption and its effect
- Use Laplace smoothing and log probabilities correctly
- Choose between multinomial, Bernoulli and Gaussian variants
- Explain why naive Bayes probabilities are often overconfident
Lesson plan
- 1 Bayes rule in words Use prior and likelihood to update a belief about a class. Start
- 2 The naive assumption Understand conditional independence and why it simplifies everything. Start
- 3 Counting and smoothing Estimate word probabilities from counts and avoid zeros. Start
- 4 Logs for stability Use log probabilities to avoid numbers too small for a computer. Start
- 5 Variants and baselines Pick the right variant and use naive Bayes as a quick baseline. Start
- 6 Knowing the blind spots Recognise overconfidence and missed interactions. Start
Try asking
About this tutor
A beginner tutor that teaches naive Bayes by building a tiny spam filter from word counts. You will apply Bayes rule, see what the 'naive' independence assumption means, add smoothing so unseen words do not break everything, and use log probabilities to keep numbers stable. Lessons compare the multinomial, Bernoulli and Gaussian variants and show why naive Bayes is a strong, quick baseline for text even though its probability estimates are often overconfident. Every lesson includes a short calculation you can do with a calculator.
Reviews
4.0
3 ratingsSample
- Leah M.Sample
Doing the spam example by hand with a calculator made Bayes rule finally stick. Smoothing makes total sense now.
- Bilal K.Sample
Short, clear lessons. The overconfidence point was new to me. Could use a slightly longer final project.
- Freya J.Sample
Good explanations but slow for me since I knew Bayes rule well. Fine if you are starting out.
About the teacher
Classical machine learning models, worked through on paper before any code
9 tutors 379 lessons taught Sample
I teach the classical machine learning models: regression, trees, ensembles, nearest neighbours, support vector machines and naive Bayes. My background is in applied analytics, where I spent a lot of time explaining to colleagues why a model made a particular prediction, and that shaped how I teach. I like to start with a tiny dataset you can hold in your...
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