Answers.Body: <p>I would say it is useful if you have an extensive knowledge in the domain you want to apply your model in. You also need more data for it to yield reasonable results.<br>
  As for real world uses l can only think of trading at the moment.</p>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2018-05-17T21:47:10.413Z
Answers.Text: Can you give me an example of where in trading it'd be used. I.e. as an observation in an RL agent used for crypto trading.
Answers.UserDisplayName: hisairnessag3
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Answers.CreationDate: 2018-05-17T14:49:52.687Z
Answers.LastActivityDate: 2018-05-17T14:49:52.687Z
Answers.UserDisplayName: Simbarashe Timothy Motsi
Body: <p>Pretty simple question here:</p>
  
  <p>Is it useful to use the standard deviation, skew, kurtosis, or any other extrapolatory stats as features, and if so in which problem sets?</p>
  
  <p>In this case, I am talking about deep learning problems.</p>
  
Tags: <machine-learning><datasets><statistical-ai>
Title: Are standard deviation, variance, skew good features for ML?
UserDisplayName: hisairnessag3
ThreadText: # Are standard deviation, variance, skew good features for ML?
  
  (Asked by: username_0 on 2018-05-16)
  
  Pretty simple question here:
  
  Is it useful to use the standard deviation, skew, kurtosis, or any other extrapolatory stats as features, and if so in which problem sets?
  
  In this case, I am talking about deep learning problems.
  
  **Tags:** machine-learning, datasets, statistical-ai
  
  
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  ## 1 answers
  
  ### Answer by username_1 on 2018-05-17. Score: 1
  
  I would say it is useful if you have an extensive knowledge in the domain you want to apply your model in. You also need more data for it to yield reasonable results.  
  As for real world uses l can only think of trading at the moment.
  
  > **Comments:**
  > **username_0**: Can you give me an example of where in trading it'd be used. I.e. as an observation in an RL agent used for crypto trading.  
  
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