Classification Space
Class 0
Class 1
Probe Point
Class-Conditional Distributions P(x|C)
P(x|C) = P(x₁|C) × P(x₂|C)
"Naive" independence assumption
Feature Distributions (1D Marginals)
Posterior Probability P(C|x)
P(C|x) = P(x|C) × P(C) / P(x)
Bayes' Theorem
Add Data Points
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Prior Probabilities P(C)
Computed from class frequencies
Model Statistics
Accuracy
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Total Points
0
Class 0
0
Class 1
0
Hover over classification space to probe
Position:
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P(C₀|x):
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P(C₁|x):
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Prediction:
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Quick Examples
How it works: Naive Bayes assumes features are conditionally independent given the class. It estimates P(x|class) from data, then uses Bayes' theorem to compute P(class|x) for classification.