Answers.Body: <p>To provide a good answer would fill several pages.  To keep it very simple try many different loss functions on your model.  Your goal is to have the highest performance based on some desired prediction metric (e.g., RMSE, MAE, MAPE, etc.).  You almost always have plenty of time to try many loss functions so you don't need to have a full understanding, and few people do, to start your project.</p>
  <p>I recommend you read the following to learn more:</p>
  <ul>
  <li><a href="https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9" rel="nofollow noreferrer">5 Regression Loss Functions All Machine Learners Should Know</a> by Prince Grover</li>
  <li><a href="https://phuctrt.medium.com/loss-functions-why-what-where-or-when-189815343d3f" rel="nofollow noreferrer">Loss functions: Why, what, where or when?</a> by Phuc Truong</li>
  <li><a href="https://analyticsindiamag.com/loss-functions-in-deep-learning-an-overview/" rel="nofollow noreferrer">Loss Functions in Deep Learning: An Overview</a></li>
  </ul>
  
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-06T17:40:43.057Z
Answers.Text: Thank you, the second page was really useful and clear. Now I understand better this topic
Answers.UserDisplayName: mariogarcia
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-06T18:01:28.450Z
Answers.Text: @mariogarcia79 You should still clarify what I asked under your post, if you want to get a more specific answer. At this point, your question has nothing to do with RNNs, but you're just asking when to use MSE as opposed to CE.
Answers.UserDisplayName: nbro
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-06T18:10:12.120Z
Answers.Text: @nbro This is what I was refering to.
Answers.UserDisplayName: mariogarcia
Answers.ContentLicense: CC BY-SA 4.0
Answers.CreationDate: 2021-02-06T16:21:57.403Z
Answers.LastActivityDate: 2021-02-06T16:30:37.240Z
Answers.LastEditDate: 2021-02-06T16:30:37.240Z
Answers.UserDisplayName: Brian O'Donnell
Body: <p>I'm relatively new to machine learning, and I don't know what error I should use for an RNN.</p>
  <p>I want to use a simple Elman RNN to predict the cases of Covid-19 there will be in a hospital for the next 15 days. I modeled this as a regression problem, treating the input like a bunch of dots in a graph to predict the tendency that the data is going to take (<em>only show if there will be more cases or less</em>).<br />
  With that bunch of dots I in fact refer to this:
  <a href="https://i.sstatic.net/6uSsY.png" rel="nofollow noreferrer"><img src="https://i.sstatic.net/6uSsY.png" alt="enter image description here" /></a></p>
  <p>Then I would treat this problem as a regression.</p>
  <p>I actually don't have anything programmed yet. Firstly I want to write it all on a paper and then get down to work.  I am also considering focusing the problem to predict the actual plot of the time-series input, but right now I want to try the regression.</p>
  <p>I've come to the conclusion that I can use these four different errors:</p>
  <ul>
  <li>MSE</li>
  <li>RMSE</li>
  <li>Entropy</li>
  <li>Cross-entropy</li>
  </ul>
  <p>What are the different characteristics of these errors? Which to use? Where and when to use them?</p>
  
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-06T14:00:44.797Z
Comments.Text: Can you clarify this part "treating the input like a bunch of dots in a graph"? Why is the data like that? What is the exact input you're using?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-06T18:16:04.003Z
Comments.Text: What does the y and x axes on your graph/plot represent?
Comments.UserDisplayName: nbro
Comments.ContentLicense: CC BY-SA 4.0
Comments.CreationDate: 2021-02-06T18:30:56.917Z
Comments.Text: The x represents the time (in days), and the y is the number of cases in that day.
Comments.UserDisplayName: mariogarcia
Tags: <recurrent-neural-networks><objective-functions><cross-entropy><mean-squared-error><root-mean-square>
Title: What error should I use for RNN?
UserDisplayName: mariogarcia
ThreadText: # What error should I use for RNN?
  
  (Asked by: username_0 on 2021-02-05)
  
  I'm relatively new to machine learning, and I don't know what error I should use for an RNN.
  
  I want to use a simple Elman RNN to predict the cases of Covid-19 there will be in a hospital for the next 15 days. I modeled this as a regression problem, treating the input like a bunch of dots in a graph to predict the tendency that the data is going to take (*only show if there will be more cases or less*).  
  With that bunch of dots I in fact refer to this:
  [![enter image description here](https://i.sstatic.net/6uSsY.png)](https://i.sstatic.net/6uSsY.png)
  
  Then I would treat this problem as a regression.
  
  I actually don't have anything programmed yet. Firstly I want to write it all on a paper and then get down to work. I am also considering focusing the problem to predict the actual plot of the time-series input, but right now I want to try the regression.
  
  I've come to the conclusion that I can use these four different errors:
  
  * MSE
  * RMSE
  * Entropy
  * Cross-entropy
  
  What are the different characteristics of these errors? Which to use? Where and when to use them?
  
  **Tags:** recurrent-neural-networks, objective-functions, cross-entropy, mean-squared-error, root-mean-square
  > **Comments:**
  > **username_1**: Can you clarify this part "treating the input like a bunch of dots in a graph"? Why is the data like that? What is the exact input you're using?  
  > **username_1**: What does the y and x axes on your graph/plot represent?  
  > **username_0**: The x represents the time (in days), and the y is the number of cases in that day.  
  
  ---
  
  ## 1 answers
  
  ### Answer by username_2 on 2021-02-06. Score: 1 | Accepted answer
  
  To provide a good answer would fill several pages. To keep it very simple try many different loss functions on your model. Your goal is to have the highest performance based on some desired prediction metric (e.g., RMSE, MAE, MAPE, etc.). You almost always have plenty of time to try many loss functions so you don't need to have a full understanding, and few people do, to start your project.
  
  I recommend you read the following to learn more:
  
  * [5 Regression Loss Functions All Machine Learners Should Know](https://heartbeat.fritz.ai/5-regression-loss-functions-all-machine-learners-should-know-4fb140e9) by Prince Grover
  * [Loss functions: Why, what, where or when?](https://phuctrt.medium.com/loss-functions-why-what-where-or-when-189815343d3f) by Phuc Truong
  * [Loss Functions in Deep Learning: An Overview](https://analyticsindiamag.com/loss-functions-in-deep-learning-an-overview/)
  
  > **Comments:**
  > **username_0**: Thank you, the second page was really useful and clear. Now I understand better this topic  
  > **username_1**: @mariogarcia79 You should still clarify what I asked under your post, if you want to get a more specific answer. At this point, your question has nothing to do with RNNs, but you're just asking when to use MSE as opposed to CE.  
  > **username_0**: @nbro This is what I was refering to.  
  
  ---
  
  