Answers.Body: <p>As the comments state without information of what you are trying to predict or the parameters it's hard to give you a precise answer.</p>
  <p>What I understand is:</p>
  <ol>
  <li>you want to predict productivity</li>
  <li>you currently have several parameters (i.e. date)</li>
  <li>you have detected overfitting of the model</li>
  </ol>
  <p>If you are dealing with time series you may want to look to something like <a href="https://en.wikipedia.org/wiki/Recurrent_neural_network" rel="nofollow noreferrer">recurrent neural networks</a> (RNN).</p>
  
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Body: <p>Sopose a productivity dataset, where day of the week and months day number are important. I'm thinking to encode these with a one-hot encoding.</p>
  <p>But if you have few years of data, that features might define a specific year+dow+day because there are few days like &quot;Mondays June 1st&quot;.</p>
  <p>What is the best way to train that situation?</p>
  <p>Would it be better to remove some fields like the day of the month?</p>
  <p>Can you train twice, once with dow and once with the day of the month and then merge with an average?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2022-07-29T20:38:09.317Z
Comments.Text: a concrete example of the type of data would be helpful to better understand your use case. It seems you have temporal data, in which case the date should be used to define a time series rather than being a feature to feed to a model. But hard to tell if that's really the case without more info.
Comments.UserDisplayName: Edoardo Guerriero
Tags: <deep-learning><training><data-preprocessing>
Title: Adding several variables that could be important but can introduce overfitting
UserDisplayName: Mquinteiro
ThreadText: # Adding several variables that could be important but can introduce overfitting
  
  (Asked by: username_0 on 2022-07-29)
  
  Sopose a productivity dataset, where day of the week and months day number are important. I'm thinking to encode these with a one-hot encoding.
  
  But if you have few years of data, that features might define a specific year+dow+day because there are few days like "Mondays June 1st".
  
  What is the best way to train that situation?
  
  Would it be better to remove some fields like the day of the month?
  
  Can you train twice, once with dow and once with the day of the month and then merge with an average?
  
  **Tags:** deep-learning, training, data-preprocessing
  > **Comments:**
  > **username_1**: a concrete example of the type of data would be helpful to better understand your use case. It seems you have temporal data, in which case the date should be used to define a time series rather than being a feature to feed to a model. But hard to tell if that's really the case without more info.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2022-07-31. Score: 0
  
  As the comments state without information of what you are trying to predict or the parameters it's hard to give you a precise answer.
  
  What I understand is:
  
  1. you want to predict productivity
  2. you currently have several parameters (i.e. date)
  3. you have detected overfitting of the model
  
  If you are dealing with time series you may want to look to something like [recurrent neural networks](https://en.wikipedia.org/wiki/Recurrent_neural_network) (RNN).
  
  > **Comments:**
  > **username_3**: Your answer could be improved with additional supporting information. Please [edit] to add further details, such as citations or documentation, so that others can confirm that your answer is correct. You can find more information on how to write good answers [in the help center](/help/how-to-answer).  
  
  ---
  
  