<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>

CC BY-SA 4.0
2021-09-06T11:17:41.940Z
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)?
nbro
CC BY-SA 4.0
2022-01-15T00:22:12.280Z
@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.
hanugm
<neural-networks><datasets>
Modelling of output neuron for mixed features?
hanugm
# 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**: @username_1 Currently, I can find the usage of mixed random variables in theory, and maybe it is difficult to find them in an application context.  

---

## 0 answers

