Naive Bayes
Also known as: Naive Bayes Sınıflandırıcı, naive bayes classifier, simple Bayes, Gaussian Naive Bayes, Multinomial Naive Bayes
Naive Bayes is a fast probabilistic classifier that applies Bayes' theorem while assuming that the features are conditionally independent given the class — a method given its standard machine-learning treatment in Tom Mitchell's 1997 textbook Machine Learning. Despite this simplifying ('naive') assumption, it is quick to train and often surprisingly accurate.
Key highlights
- Extremely fast to train and to predict, even on large or high-dimensional data such as text.
- Works well as a simple, low-cost baseline and often performs surprisingly well despite its naive assumption.
- Handles categorical, binary, continuous, and text features through its different variants.
- Needs relatively little training data to estimate its parameters.
Intuition
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How it works
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When to use it
A good fit for fast classification when you have categorical, binary, continuous, or text features and at least about 30 observations, especially as a strong, cheap baseline. It is assumption-light on distribution overall but each variant carries its own likelihood assumption (e.g. within-class normality for the Gaussian variant), and it is sensitive to class imbalance. When features are strongly correlated or you need calibrated effect estimates, prefer logistic regression.
Strengths & limitations
- Extremely fast to train and to predict, even on large or high-dimensional data such as text.
- Works well as a simple, low-cost baseline and often performs surprisingly well despite its naive assumption.
- Handles categorical, binary, continuous, and text features through its different variants.
- Needs relatively little training data to estimate its parameters.
- The conditional-independence assumption rarely holds, so probability estimates can be poorly calibrated even when the ranking is good.
- Sensitive to class imbalance: priors must be adjusted or the data resampled.
- Gaussian Naive Bayes degrades when the within-class normality assumption is violated.
- Unreliable with very small samples, where per-class probability estimates are not trustworthy.
Common pitfalls
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Applications
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Frequently asked
Why is it called 'naive'?
Because it assumes the features are conditionally independent given the class — that is, that knowing one feature tells you nothing about another within a class. This is rarely literally true, but the simplification makes the model trivial to estimate and fast to run, and the classifier often still ranks classes accurately.
Which variant should I use?
Gaussian Naive Bayes is for continuous features assumed normally distributed within each class. Multinomial Naive Bayes suits count or term-frequency data, as in text classification, and does not accept negative values. Match the variant to your feature type.
Can I trust the predicted probabilities?
Use the predicted class with care as a ranking. Because the independence assumption is usually violated, the raw probabilities tend to be pushed toward 0 or 1 and are often poorly calibrated, even when the chosen class is correct.
How much data does it need?
It is economical with data, but you still need enough observations per class — at least roughly 30 overall — so that the class priors and per-feature likelihoods can be estimated reliably. Below that, logistic regression is a safer choice.
Sources
- 1.Mitchell, T. M. (1997). Machine Learning. McGraw-Hill.ISBN 978-0070428072
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Cite this page
ScholarGate. (2026, June 1). Naive Bayes. ScholarGate. https://scholargate.app/machine-learning/naive-bayes