Take the tour ×  Could someone provide differences between these
two techniques? Also, I would very much appreciate if you could
give an example of a ML method that falls into discriminative
model or gnerative one. For instance, is Perceptron
discriminative? K-Means?  share|improve this question add
comment  1 Answer  up vote 2 down vote accepted  Let $X$ be your
observed data and let $Y$ be their unobserved/hidden properties.
In a ML setting, $Y$ usually holds the categories of $X$.  A
generative model models their joint distribution, $P(X,Y)$.  A
discriminative model models the posterior probability of the
categories, $P(Y|X)$.  Depending on what you want to do, you
choose between generative versus discriminative modeling. For
example, if you are interested in doing classification, a
discriminative model would be your choice because you are
interested in $P(Y|X)$. However, you can use a generative model,
$P(X,Y)$, for classification, too.  A generative model allows
you to generate synthetic data ($X$) using