Naive Bayes — When the Wrong Assumption Wins

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Learn how Naive Bayes, a simple linear classifier, can outperform more complex models despite making a false assumption about data independence.

intermediate Published 9 May 2026

Action Steps

Apply Bayes' rule to compute P(class | features) using Naive Bayes
Assume conditional independence of features given the class
Use counting to simplify the computation of probabilities
Compare Naive Bayes to logistic regression and other linear classifiers
Evaluate the performance of Naive Bayes on text classification problems

Who Needs to Know This

Data scientists and machine learning engineers can benefit from understanding Naive Bayes, as it is a widely used and effective algorithm for text classification and other problems.

Key Insight

💡 Naive Bayes makes an obviously false assumption about data independence, but still manages to outperform more sophisticated models on certain problems.

Key Takeaways

Learn how Naive Bayes, a simple linear classifier, can outperform more complex models despite making a false assumption about data independence.

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Title: Naive Bayes — When the Wrong Assumption Wins

URL Source: https://grahamjroy.medium.com/naive-bayes-when-the-wrong-assumption-wins-e4b5a43e4e60?source=rss------artificial_intelligence-5

Published Time: 2026-05-09T13:13:52Z

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# Naive Bayes — When the Wrong Assumption Wins | by Graham Roy | May, 2026 | Medium

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# Naive Bayes — When the Wrong Assumption Wins

## Algorithms in Python — Supervised Learning, Part 3

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In Part 2 we trained a logistic regression by gradient descent on log loss, and the model that came out was a linear classifier: sigmoid of a weighted sum of features. Today we look at another linear classifier that gets to the same shape via a completely different route — no gradient descent, no iteration, just _counting_. It is one of the oldest algorithms in machine learning, it makes an obviously false assumption about the data, and on a class of problems — especially text classification — it beats more sophisticated models with embarrassing regularity.

The algorithm is **Naive Bayes**. The “Bayes” part comes from Bayes’ rule, which the model applies directly to compute `P(class | features)`. The "naive" part is the assumption that all features are _conditionally independent given the class_ — an assumption that is almost never true in p

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Naive Bayes — When the Wrong Assumption Wins

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