Answers.Body: <p>For extra input that does not matter, you should not input it to the network.</p>
  
  <blockquote>
    <p>Feature selection, the process of finding and selecting the most
    useful features in a dataset, is a crucial step of the machine
    learning pipeline. Unnecessary features <strong>decrease training speed</strong>,
    <strong>decrease model interpretability</strong>, and, most importantly, <strong>decrease
    generalization performance</strong> on the test set.</p>
  </blockquote>
  
  <p>Source: <a href="https://towardsdatascience.com/a-feature-selection-tool-for-machine-learning-in-python-b64dd23710f0" rel="nofollow noreferrer">A Feature Selection Tool for Machine Learning in Python</a></p>
  
  <p>As the source says, unnecessary features decreases accuracy and training speed. Moreover, they have no mapping to the labels, so they won't end up being used. They are unnecessary and adding them will only cause you trouble. Hope this helps you and have a nice day!</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2019-11-02T15:16:35.167Z
Answers.LastActivityDate: 2019-11-02T16:10:18.650Z
Answers.LastEditDate: 2019-11-02T16:10:18.650Z
Answers.UserDisplayName: Clement
Body: <p>For example, given a face image, and you want to predict the gender. You also have age information for each person, should you feed the age information as input or should you use it as auxiliary output that the network needs to predict?</p>
  
  <p>How do I know analytically (instead of experimentally) which approach will be better? What is the logic behind this?</p>
  
Tags: <neural-networks><machine-learning><deep-learning><convolutional-neural-networks><feature-selection>
Title: Should I use my redundant feature as an auxiliary output or as another input feature?
UserDisplayName: offchan
ThreadText: # Should I use my redundant feature as an auxiliary output or as another input feature?
  
  (Asked by: username_0 on 2019-11-02)
  
  For example, given a face image, and you want to predict the gender. You also have age information for each person, should you feed the age information as input or should you use it as auxiliary output that the network needs to predict?
  
  How do I know analytically (instead of experimentally) which approach will be better? What is the logic behind this?
  
  **Tags:** neural-networks, machine-learning, deep-learning, convolutional-neural-networks, feature-selection
  
  ---
  
  ## 1 answers
  
  ### Answer by username_1 on 2019-11-02. Score: 2 | Accepted answer
  
  For extra input that does not matter, you should not input it to the network.
  
  > Feature selection, the process of finding and selecting the most
  > useful features in a dataset, is a crucial step of the machine
  > learning pipeline. Unnecessary features **decrease training speed**,
  > **decrease model interpretability**, and, most importantly, **decrease
  > generalization performance** on the test set.
  
  Source: [A Feature Selection Tool for Machine Learning in Python](https://towardsdatascience.com/a-feature-selection-tool-for-machine-learning-in-python-b64dd23710f0)
  
  As the source says, unnecessary features decreases accuracy and training speed. Moreover, they have no mapping to the labels, so they won't end up being used. They are unnecessary and adding them will only cause you trouble. Hope this helps you and have a nice day!
  
  
  ---
  
  