Body: <p>A dataset in artificial intelligence, in general, consists of some features (say <span class="math-container">$n$</span>). Assume that <span class="math-container">$m$</span> among them are output features. I want to model this function using a neural network. So, input to my neural network is <span class="math-container">$n-m$</span> features and output is <span class="math-container">$m$</span> features. My question is about the output features.</p>
  <p>If an output feature is a continuous random variable, then its corresponding output neuron can be trained to give continuous output. Similarly, if an output feature is a discrete random variable, then its corresponding output neuron can be trained to give discrete output.</p>
  <p>But, I never came across the features that are <a href="https://www.probabilitycourse.com/chapter4/4_3_1_mixed.php" rel="nofollow noreferrer">mixed random variables</a>. What is the nature of the output of the neuron that is intended to give the output value for a mixed random variable, which is neither discrete nor continuous in nature?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-09-06T11:17:41.940Z
Comments.Text: Can you please provide an example of a feature that is neither continuous nor discrete? Because, right now, I don't understand what you mean by that. Moreover, it may be a good idea to explain why you're interested in this question. Which problem are you having that involves such "neither continuous nor discrete features" (whatever that means)?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-01-15T00:22:12.280Z
Comments.Text: @nbro Currently, I can find the usage of mixed random variables in theory, and maybe it is difficult to find them in an application context.
Comments.UserDisplayName: hanugm
Tags: <neural-networks><datasets>
Title: Modelling of output neuron for mixed features?
UserDisplayName: hanugm
ThreadText: # Modelling of output neuron for mixed features?
  
  (Asked by: username_0 on 2021-09-05)
  
  A dataset in artificial intelligence, in general, consists of some features (say $n$). Assume that $m$ among them are output features. I want to model this function using a neural network. So, input to my neural network is $n-m$ features and output is $m$ features. My question is about the output features.
  
  If an output feature is a continuous random variable, then its corresponding output neuron can be trained to give continuous output. Similarly, if an output feature is a discrete random variable, then its corresponding output neuron can be trained to give discrete output.
  
  But, I never came across the features that are [mixed random variables](https://www.probabilitycourse.com/chapter4/4_3_1_mixed.php). What is the nature of the output of the neuron that is intended to give the output value for a mixed random variable, which is neither discrete nor continuous in nature?
  
  **Tags:** neural-networks, datasets
  > **Comments:**
  > **username_1**: Can you please provide an example of a feature that is neither continuous nor discrete? Because, right now, I don't understand what you mean by that. Moreover, it may be a good idea to explain why you're interested in this question. Which problem are you having that involves such "neither continuous nor discrete features" (whatever that means)?  
  > **username_0**: @nbro Currently, I can find the usage of mixed random variables in theory, and maybe it is difficult to find them in an application context.  
  
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  ## 0 answers
  
  