Naive Bayes

Summary

A fast, simple classifier based on Bayes' theorem, assuming features are independent (an assumption that's often technically wrong but works surprisingly well in practice) — especially strong for text classification.

How to Learn This

1Fit a Naive Bayes classifier on a simple text classification dataset.
2Learn why the 'naive' independence assumption rarely holds, yet the model still performs well.
3Compare its speed to a more complex model on the same dataset.

Support Vector Machines

Build & Compare 3 ML Models on the Same Dataset

More topics in Machine Learning Fundamentals

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