[Verse 1] Started with a problem, classification on my mind Got features and labels, need a pattern I can find Bayes theorem foundation, probability the key Independent assumptions make it work efficiently Prior times likelihood, divided by evidence Simple math equation gives me confidence Naive because we assume features don't connect But still delivers results that I can respect [Chorus] Naive Bayes, calculate the ways Prior times likelihood, that's how it plays Naive Bayes, independent maze Each feature stands alone, through probability's rays Multiply the pieces, normalize the score Highest probability wins, that's what we're looking for [Verse 2] Training phase collecting, count up every class Document frequency shows me what will pass Gaussian for continuous, multinomial discrete Bernoulli for binary makes the model complete Smoothing helps with zeros, Laplace addition One to every count prevents math collision Text classification, spam detection strong Medical diagnosis, where it belongs [Chorus] Naive Bayes, calculate the ways Prior times likelihood, that's how it plays Naive Bayes, independent maze Each feature stands alone, through probability's rays Multiply the pieces, normalize the score Highest probability wins, that's what we're looking for [Bridge] Fast to train, fast to predict Memory efficient, performance slick Baseline model, benchmark test When features independent, it performs its best Categorical natural language processing king Email filtering, sentiment everything [Verse 3] Prediction time arriving, new data at the door Calculate each class probability score Argmax function chooses winner from the race Conditional independence, assumptions we embrace Works with small datasets, scales up really well Interpretable results, story it can tell Linear decision boundary drawn in feature space Simple yet effective, computational grace [Chorus] Naive Bayes, calculate the ways Prior times likelihood, that's how it plays Naive Bayes, independent maze Each feature stands alone, through probability's rays Multiply the pieces, normalize the score Highest probability wins, that's what we're looking for [Outro] When assumptions hold true, Naive Bayes will shine Probabilistic classifier, by mathematical design Prior times likelihood, forever in my head Naive but not foolish, strong foundation instead
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