Introduction
Introduction to Naive Bayes, a probabilistic algorithm ideal for classification tasks.
Theory
Explore the theoretical background of Naive Bayes, including Bayes' Theorem and independence assumptions.
scikit-learn Example
Build a leakage-safe text-classification pipeline with MultinomialNB.
TensorFlow Guidance
Understand why classical Naive Bayes is not a Keras training problem.
PyTorch Guidance
Choose a probabilistic estimator instead of hand-writing Naive Bayes with PyTorch tensors.
Common Mistakes & Best Practices
Match the likelihood to the features and avoid leakage, invalid scaling, and overconfident probabilities.
Comparison with Other Algorithms
Compare Naive Bayes with logistic regression, trees, SVMs, and ensembles.