🔥 TOP 10 Priority - Most popular ML algorithm #5

Overview

Implement Naive Bayes classifier family, probabilistic classifiers based on Bayes' theorem with independence assumptions.

Implementation Details

• GaussianNB: Features follow normal distribution
• MultinomialNB: For discrete counts (text classification)
• BernoulliNB: For binary/boolean features
• Laplace smoothing
• Partial fit for incremental learning

References

• Fast and efficient
• Works well with high-dimensional data
• Excellent baseline for text classification
• Probabilistic predictions

Acceptance Criteria

• GaussianNB struct
• MultinomialNB struct (optional)
• fit/predict/predict_proba
• Laplace smoothing parameter
• Comprehensive tests (EXTREME TDD)
• Example: naive_bayes_spam.rs or naive_bayes_iris.rs
• Book chapter: ml-fundamentals/naive-bayes.md

Priority Justification

Naive Bayes is #5 in industry usage per Analytics Vidhya 2025 ranking

Why it's popular:

• Extremely fast training
• Works well with small datasets
• Standard baseline for text classification