[Verse 1]
Data scattered like confetti on the floor
Each feature tells a story, independence at the core
Conditional probabilities dancing in equations
Multiply the likeliness across all observations
Bayes theorem flipped the script, turned tables on prediction
Prior knowledge meets new evidence for classification
[Chorus]
Naive assumption keeps it clean
Features independent, purely pristine
Prior times likelihood divided by evidence scene
Naive Bayes classifier, mathematical machine
Multiply probabilities, argmax routine
Posterior decides which class reigns supreme
[Verse 2]
Training phase constructs the probability tables tight
Count frequencies for each feature given class insight
Laplace smoothing prevents that zero disaster zone
Add-one correction keeps denominators grown
Text classification loves this algorithm's speed
Spam detection, sentiment - fulfills every need
[Chorus]
Naive assumption keeps it clean
Features independent, purely pristine
Prior times likelihood divided by evidence scene
Naive Bayes classifier, mathematical machine
Multiply probabilities, argmax routine
Posterior decides which class reigns supreme
[Bridge]
Gaussian distributions handle continuous data streams
Multinomial tackles counts and frequency schemes
Bernoulli binary features, presence or absence calls
Three variants adapting as different data falls
[Verse 3]
Prediction time arrives with unlabeled specimen
Calculate each class probability, comparing every den
Logarithms prevent underflow computational curse
Sum of logs beats products, numerical universe
Maximum a posteriori crowns the winning team
Conditional independence assumption - naive but keen
[Outro]
Simple yet effective, scales to massive size
Baseline benchmark challenger, consistently surprised
When features correlate poorly, naive fails the test
But often outperforms complex methods - simplicity blessed