<p>It's hard nowadays to draw a distinct line between the two with the advent of conditional generation and normalizing flow</p>
<p>Usually, we say that is generative if you try to model the joint probability <span class="math-container">$p(x,y)$</span>, whereas discriminative models try to model the marginal <span class="math-container">$p(y|x)$</span></p>
<p>However, generative model aims at sampling from such learned distribution, where usually instead discriminative models are only interested in some statistics of it (such as the mode)</p>

CC BY-SA 4.0
2024-06-19T07:29:00.897Z
I would lead with and emphasize *sampling*, a lot of sequence generators are predictive models of distribution, plus a separate sampling routine
Neil Slater
CC BY-SA 4.0
2024-06-19T10:13:57.843Z
@NeilSlater yup, though they start from the hypothesis of modelling $p(x)$ and they factor it as $\prod p(x_i | x_{j<i})$, and so it falls back to the usual classification problem
Alberto
CC BY-SA 4.0
2024-06-18T22:17:49.737Z
2024-06-18T22:17:49.737Z
Alberto
<p>How does one tell if a given model is generative AI or predictive AI?</p>
<p>Do generative models have more outputs than inputs and <em>vice versa</em> for predictive models?</p>

<terminology><definitions><generative-model><prediction><predictive-model>
How to tell if a model is generative vs. predictive?
Geremia
# How to tell if a model is generative vs. predictive?

(Asked by: username_0 on 2024-06-18)

How does one tell if a given model is generative AI or predictive AI?

Do generative models have more outputs than inputs and *vice versa* for predictive models?

**Tags:** terminology, definitions, generative-model, prediction, predictive-model


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## 1 answers

### Answer by username_1 on 2024-06-18. Score: 2

It's hard nowadays to draw a distinct line between the two with the advent of conditional generation and normalizing flow

Usually, we say that is generative if you try to model the joint probability $p(x,y)$, whereas discriminative models try to model the marginal $p(y|x)$

However, generative model aims at sampling from such learned distribution, where usually instead discriminative models are only interested in some statistics of it (such as the mode)

> **Comments:**
> **username_2**: I would lead with and emphasize *sampling*, a lot of sequence generators are predictive models of distribution, plus a separate sampling routine  
> **username_1**: @NeilSlater yup, though they start from the hypothesis of modelling $p(x)$ and they factor it as $\prod p(x_i | x_{j<i})$, and so it falls back to the usual classification problem  

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