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Are there are any supervised ML techniques that take into account the distribution of target variables? In Statistics, quantile regressions are a good way to model the data based on conditional distribution of dependent variables.

I am curious if there are any techniques in ML literature that would be a good choice for modeling based on conditional distribution of dependent variable data?

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    $\begingroup$ What about a quantile regression neural network? $\endgroup$
    – Dave
    Commented Jan 2, 2022 at 22:58
  • $\begingroup$ Doesn't the ML literature say anything about maximum likelihood? $\endgroup$
    – BigBendRegion
    Commented Jan 3, 2022 at 0:19
  • $\begingroup$ Yes, ensemble techniques and neural networks can impose distribution on the target. $\endgroup$
    – patagonicus
    Commented Jan 3, 2022 at 3:46
  • $\begingroup$ @MehmetSüzen could you elaborate on how they impose distribution on the target variable? $\endgroup$
    – kms
    Commented Jan 3, 2022 at 7:10
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    $\begingroup$ @jbowman What does it even mean for linear regression to be done via neural networks? $\endgroup$
    – Dave
    Commented Jan 4, 2022 at 3:47

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Yes, but be aware regression is also a machine learning method. What you're looking to learn more about are loss functions, and you want to use a quantile loss function. You can usually specify your own loss function. In sklearn for example, you might have a loss parameter for your estimator, such as in sgdregressor.

See here for more info Quantile regression: Loss function

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