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Understanding Latent Dirichlet Allocation (LDA) — A Data Scientist’s Guide (Part 1)
LDA Explained with a Dog Pedigree Model
7 min readFeb 9, 2024
Machine learning algorithms are now so accessible that even my non-technical wife constantly asks: “Isn’t that what ChatGPT is capable of?”
The time has come for data scientists to remain vigilant on the whys and hows behind machine learning algorithms.
This 3-part blog post is an actual journey where I have attempted to explain to my wife how Latent Dirichlet Allocation (LDA, a staple in all data scientists’ arsenal for topic modelling, recommendation and more) works with the help of a dog pedigree model. By the end of the series, you should be able to answer the following:
Part 1 (We are here now!):
- How does LDA work?
- How to explain LDA to a non-technical person?
Part 2 (link):
- How does LDA improve iteratively?
- How does LDA converge?
- Bonus: Get your LDA cheatsheet here!
Part 3:
- When to use LDA & when not to?
- How can we use it in Python?
- What are the alternatives & variants to LDAs (excluding LLMs)?
