- Discriminative and generative models have distinct differences,
- Discriminative methods are simpler but not necessarily better,
- This One Picture outlines a few major differences between the methods, along with a few examples and use cases.

Read any textbook on the difference between generative and discriminative models and you'll usually find the explanation is usually less than intuitive. One of the better explanations I've read [1] is the following analogy: you are talking to someone who is speaking a language you don't understand. Your task is to figure out what language they are speaking (without the use of Google Translate!). You have two options:

**Generative method:**Learn each language and then use that knowledge to determine which language is being spoken.**Discriminative method**: Determine the linguistic differences without actually learning the language.

As you may be able to tell from this simple analogy. *discriminative methods are much simpler.* But there are situations when you might want to use generative methods instead. For example, generative models are much better for finding missing values (i.e. they can generate data). However, if classification accuracy is your goal, a discriminative method, which "discriminates" (classifies) is likely the better choice. In sum:

References

[1] Machine Learning: Generative and Discriminative Models

[2] Reasoning about Missing Data in Machine Learning

[3] Jebara, T. (2019). Machine Learning Discriminative and Generative. Springer.

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Posted 1 March 2021

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